Device, system and method for predicting battery consumption of electric vehicle

ABSTRACT

A system and a method for predicting a battery consumption of an electric vehicle are disclosed. The battery consumption prediction system of the electric vehicle predicts the battery consumption considering an overall state of the electric vehicle and an external environment of the electric vehicle. The battery consumption prediction system of the electric vehicle may be associated with an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

CROSS-REFERENCE TO RELATED APPLICATION(S)

Pursuant to 35 U.S.C. § 119(a), this application claims the benefit ofearlier filing date and right of priority to Korean Patent ApplicationNo. 10-2019-0144802, filed on Nov. 13, 2019, the contents of which arehereby incorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to a device, a system and a method forpredicting a battery consumption of an electric vehicle.

Discussion of the Related Art

The vehicle is one of transportation means for moving a user riding inthe vehicle in a desired direction, and a representative example of thevehicle may include an automobile. In particular, the automobilerequires a driving force for the movement in order to provide the userwith the convenience of movement.

In a related art, the automobile used an internal combustion engine toobtain the driving force, but electric vehicles driven by electric powerstored in batteries have recently emerged.

In order to know an available time or distance of the battery embeddedin the electric vehicle in the related art, the related art measured avoltage or a current of the battery itself and calculated the availabletime or distance based on the measured voltage or current.

However, the available time or distance of the battery took no accountof factors which affect the battery usage, for example, an overall stateof the electric vehicle such as a driver's driving pattern, or anexternal environment of the electric vehicle such as weather and trafficconditions. Therefore, the available time or distance of the battery wasprovided inaccurately.

SUMMARY OF THE INVENTION

An object of the present disclosure is to address the above-describedand other needs and/or problems.

The present disclosure provides a device, a system and a method forpredicting a battery consumption of an electric vehicle considering allof information about a battery itself, information about an externalenvironment of the electric vehicle such as weather or trafficsituation, and information about an overall state of the electricvehicle such as a drive mode, the number of occupants, a weight ofloaded load, etc. of the electric vehicle.

The present disclosure provides a device, a system and a method forpredicting a battery consumption of an electric vehicle capable ofsetting and changing reliability of a predicted consumption of a batteryby comparing an actual consumption and the predicted consumption of thebattery.

In one aspect of the present disclosure, there is provided a batteryconsumption prediction device of an electric vehicle comprising aprocessor configured to calculate a battery consumption of the electricvehicle, wherein the processor includes a collection module configuredto collect first information indicating an overall state of the electricvehicle and second information indicating an external environment of theelectric vehicle and generate prediction data based on the firstinformation and the second information, and a prediction moduleconfigured to receive the prediction data from the collection module andderive a predicted consumption of a battery.

The prediction module may be configured to obtain a difference betweenthe predicted consumption and an actual consumption that is calculatedby measuring in real time the battery of the electric vehicle, and anabsolute value of the difference, provide a first feedback reducing areliability of the predicted consumption if the absolute value exceeds afirst value, and provide a second feedback increasing the reliability ofthe predicted consumption if the absolute value is equal to or less thanthe first value.

The processor may further include a learning module that is connected tobe able to communicate data with each of the collection module and theprediction module. The learning module may be configured tomachine-learn the first information, the second information, thepredicted consumption, and the actual consumption and give thereliability to the predicted consumption according to a magnitude of thedifference between the predicted consumption and the actual consumption,i.e., a magnitude of the absolute value.

The prediction module may be configured to output a consumption tablethat uses the prediction data as an input value and uses the predictedconsumption as a result value.

The consumption table may include a first item unit into which the firstinformation and the second information are inserted, a second item unitindicating the predicted consumption as a result of the first item unit,and a third item unit indicating the reliability of the predictedconsumption displayed on the second item unit.

The reliability may be expressed as a natural number. The first feedbackmay be a feedback for adding ‘−1’ to the reliability, and the secondfeedback may be a feedback for adding ‘+1’ to the reliability.

The prediction module may be configured to output a consumption tablethat uses the prediction data as an input value and uses the predictedconsumption as a result value, and add the first feedback and the secondfeedback accumulated on the reliability to calculate a feedback sum.

The prediction module may be configured to delete the predictedconsumption corresponding to the feedback sum from the consumption tableif the feedback sum is less than a second value that is set to one of −5to −10.

The prediction module may be configured to add the predicted consumptioncorresponding to the feedback sum to the consumption table if thefeedback sum is greater than a second value that is set to one of −5 to−1.

The first value may be set to one of 5 to 10.

The collection module may be connected to be able to communicate datawith at least one of a sensing unit, a communication unit, an objectdetector, a driving operator, a vehicle driver, a location datagenerator, a navigation, and a main electronic control unit (ECU) of theelectric vehicle. If a unit time or a unit distance has passed, thecollection module may be configured to collect the first information andthe second information from at least one of the sensing unit, thecommunication unit, the object detector, the driving operator, thevehicle driver, the location data generator, the navigation, and themain ECU of the electric vehicle.

The unit time may be set to one of 1 minute to 5 minutes, and the unitdistance may be set to one of 1 km to 5 km.

The first information may include a drive mode, a drive speed, a numberof occupants, a weight of loaded load, center of gravity, a rapidacceleration history and a rapid deceleration history of the electricvehicle, and a temperature, a usage period, an output, a capacity and alife of the battery.

The second information may include a current time, a temperature and aweather around the electric vehicle at the current time, and a trafficstate of a route on which the electric vehicle is driving.

The battery consumption prediction device may further comprise an outputunit configured to display a battery power level calculated based on thepredicted consumption or the actual consumption and display a drivabledistance of the electric vehicle based on the battery power level.

In another aspect of the present disclosure, there is provided a batteryconsumption prediction system of an electric vehicle comprising acollection device configured to collect first information indicating anoverall state of the electric vehicle and second information indicatingan external environment of the electric vehicle and generate predictiondata, a prediction server configured to derive a predicted consumptionof a battery based on the prediction data transmitted from thecollection device, and a user equipment configured to display a resultcalculated by the prediction server, wherein the prediction server isconfigured to calculate a difference between the predicted consumptionand an actual consumption of the battery of the electric vehicle andgenerate a feedback changing a reliability of the predicted consumption.

The collection device may include a processor configured to collect rawdata of the electric vehicle as the first information, preprocess thefirst information, and generate the prediction data. The processor maybe connected to be able to communicate data with at least one of asensing unit, a communication unit, an object detector, a drivingoperator, a vehicle driver, a location data generator, a navigation, anda main electronic control unit (ECU) of the electric vehicle.

The processor may be configured to, periodically or each time theelectric vehicle drives a predetermined distance, collect the raw datafrom at least one of the sensing unit, the communication unit, theobject detector, the driving operator, the vehicle driver, the locationdata generator, the navigation, and the main ECU and collect the secondinformation from an external server.

The first information may include a drive mode, a drive speed, a numberof occupants, a weight of loaded load, center of gravity, a rapidacceleration history and a rapid deceleration history of the electricvehicle, and a temperature, a usage period, an output, a capacity and alife of the battery. The second information may include a current time,a temperature and a weather around the electric vehicle at the currenttime, and a traffic state of a route on which the electric vehicle isdriving.

The prediction server may include a learning module configured tomachine-learn the first information and the second information, that arefactors capable of changing the predicted consumption and the actualconsumption, in association with the predicted consumption and theactual consumption, and a prediction module configured to output aconsumption table that uses the prediction data as an input value anduses the predicted consumption as a result value.

The prediction module may be configured to obtain a difference betweenthe predicted consumption and the actual consumption that is calculatedby measuring in real time the battery of the electric vehicle, and anabsolute value of the difference, provide a first feedback reducing thereliability of the predicted consumption if the absolute value exceeds afirst value, and provide a second feedback increasing the reliability ofthe predicted consumption if the absolute value is equal to or less thanthe first value.

The battery consumption prediction system may further comprise anexternal server configured to transmit the second information to thecollection device.

In another aspect of the present disclosure, there is provided a methodfor predicting a battery consumption of an electric vehicle, the methodcomprising collecting first information and second information,preprocessing the first information and the second information togenerate prediction data, deriving a predicted consumption of a batteryof the electric vehicle using the prediction data as an input value,measuring in real time a battery power level of the electric vehicle andsubtracting the real-time battery power level from an initial batterypower level to calculate an actual consumption, obtaining a differencebetween the predicted consumption and the actual consumption and anabsolute value of the difference, and evaluating a reliability of thepredicted consumption according to a magnitude of the absolute value.

The evaluating of the reliability may comprise applying a first feedbackreducing the reliability of the predicted consumption if the absolutevalue exceeds a first value, and applying a second feedback increasingthe reliability of the predicted consumption if the absolute value isequal to or less than the first value.

The first value may be set to one of 5 to 10.

The first feedback may be a feedback for adding ‘−1’ to the reliability,and the second feedback may be a feedback for adding ‘+1’ to thereliability.

The deriving of the predicted consumption may comprise creating aconsumption table that uses the prediction data as an input value anduses the predicted consumption as a result value, inputting theprediction data to the consumption table, outputting the predictedconsumption as a result value, searching a reliability evaluationhistory and checking whether there is a previous reliability evaluationresult corresponding to the predicted consumption output as the resultvalue, and if the previous reliability evaluation result exists in thereliability evaluation history, giving and displaying a reliabilityincluded in the previous reliability evaluation result to the predictedconsumption.

The method may further comprise, after evaluating the reliability,adding a first feedback and a second feedback accumulated on thereliability to calculate a feedback sum, and deleting the predictedconsumption corresponding to the feedback sum from the consumption tableif the feedback sum is less than a second value.

The second value may be set to one of −5 to −10.

The method may further comprise, after calculating the feedback sum,adding the predicted consumption corresponding to the feedback sum tothe consumption table if the feedback sum is greater than the secondvalue.

The method may further comprise, after evaluating the reliability,calculating a current battery power level of the electric vehicle basedon the predicted consumption or the actual consumption, calculating adrivable distance of the electric vehicle based on the current batterypower level, and displaying the drivable distance to a driver.

The method may further comprise, before collecting the first informationand the second information, inputting a destination to a navigation ofthe electric vehicle, outputting at least one route for reaching thedestination, and collecting third information about the route.

The deriving of the predicted consumption may comprise calculating apredicted battery consumption with respect to the route based on thethird information, and displaying, to the driver, a total batteryconsumption consumed to complete the route. The third information mayinclude a total length of the route, a type of road installed in theroute, and a slope, an altitude above sea level, an altitude deviationand a terrain for each section included in the route.

The battery consumption prediction device, system, and method of theelectric vehicle according to the present disclosure predict a batteryconsumption considering all factors that may affect the batteryconsumption, i.e., all of information about a battery itself,information about an external environment of the electric vehicle suchas weather or traffic situation, and information about an overall stateof the electric vehicle such as a drive mode, the number of occupants, aweight of loaded load, etc. of the electric vehicle, and thus canaccurately predicts the actual consumption of the battery consumed whilethe electric vehicle is driving.

Further, the battery consumption prediction device, system, and methodof the electric vehicle according to the present disclosure exclude aninaccurate predicted consumption from the learning module for derivingthe predicted consumption by evaluating the reliability of the predictedconsumption, and thus can provide the predicted consumption equal orsimilar to the actual consumption.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure, illustrate embodiments of thedisclosure and together with the description serve to explain theprinciple of the disclosure.

FIG. 1 is a block diagram illustrating configuration of a wirelesscommunication system to which methods proposed in the present disclosureare applicable.

FIG. 2 illustrates an example of a signal transmission/reception methodin a wireless communication system.

FIG. 3 illustrates an example of basic operation of a user equipment anda 5G network in a 5G communication system.

FIG. 4 illustrates a vehicle according to an embodiment of the presentdisclosure.

FIG. 5 is a block diagram of an AI device according to an embodiment ofthe present disclosure.

FIG. 6 illustrates a system, in which a vehicle is associated with an AIdevice, in accordance with an embodiment of the present disclosure.

FIG. 7 is a block diagram illustrating configuration of a batteryconsumption prediction device of an electric vehicle according to afirst embodiment of the present disclosure.

FIG. 8 is a block diagram illustrating configuration of a batteryconsumption prediction device of an electric vehicle according to asecond embodiment of the present disclosure.

FIG. 9 is a block diagram illustrating configuration of a batteryconsumption prediction device of an electric vehicle according to athird embodiment of the present disclosure.

FIG. 10 is a block diagram illustrating configuration of a batteryconsumption prediction system of an electric vehicle according to afirst embodiment of the present disclosure.

FIG. 11 is a block diagram illustrating configuration of a batteryconsumption prediction system of an electric vehicle according to asecond embodiment of the present disclosure.

FIG. 12 illustrates a consumption table of an electric vehicle batteryaccording to an embodiment of the present disclosure.

FIG. 13 is a flow chart illustrating a method for predicting a batteryconsumption of an electric vehicle according to an embodiment of thepresent disclosure.

FIG. 14 is a flow chart illustrating a method for deriving a predictedbattery consumption according to an embodiment of the presentdisclosure.

FIG. 15 is a flow chart illustrating a method for evaluating reliabilityof a predicted battery consumption according to an embodiment of thepresent disclosure.

FIG. 16 is a flow chart illustrating a method for displaying a drivabledistance according to an embodiment of the present disclosure.

FIG. 17 is a flow chart illustrating a method for predicting a batteryconsumption of an electric vehicle according to another embodiment ofthe present disclosure.

FIG. 18 is a flow chart illustrating a method for deriving a predictedbattery consumption according to another embodiment of the presentdisclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the attached drawings. The same or similar componentsare given the same reference numbers and redundant description thereofis omitted. The suffixes “module” and “unit” of elements herein are usedfor convenience of description and thus can be used interchangeably anddo not have any distinguishable meanings or functions. Further, in thefollowing description, if a detailed description of known techniquesassociated with the present disclosure would unnecessarily obscure thegist of the present disclosure, detailed description thereof will beomitted. In addition, the attached drawings are provided for easyunderstanding of embodiments of the disclosure and do not limittechnical spirits of the disclosure, and the embodiments should beconstrued as including all modifications, equivalents, and alternativesfalling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describevarious components, such components must not be limited by the aboveterms. The above terms are used only to distinguish one component fromanother.

When an element is “coupled” or “connected” to another element, itshould be understood that a third element may be present between the twoelements although the element may be directly coupled or connected tothe other element. When an element is “directly coupled” or “directlyconnected” to another element, it should be understood that no elementis present between the two elements.

The singular forms are intended to include the plural forms as well,unless the context clearly indicates otherwise.

In addition, in the disclosure, it will be further understood that theterms “comprise” and “include” specify the presence of stated features,integers, steps, operations, elements, components, and/or combinationsthereof, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or combinations.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

Referring to FIG. 1, a device (autonomous device) including anautonomous module is defined as a first communication device (910 ofFIG. 1), and a processor 911 can perform detailed autonomous operations.

A 5G network including another vehicle communicating with the autonomousdevice is defined as a second communication device (920 of FIG. 1), anda processor 921 can perform detailed autonomous operations.

The 5G network may be represented as the first communication device andthe autonomous device may be represented as the second communicationdevice.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,an autonomous device, or the like.

For example, a terminal or user equipment (UE) may include a vehicle, acellular phone, a smart phone, a laptop computer, a digital broadcastterminal, personal digital assistants (PDAs), a portable multimediaplayer (PMP), a navigation device, a slate PC, a tablet PC, anultrabook, a wearable device (e.g., a smartwatch, a smart glass and ahead mounted display (HMD)), etc. For example, the HMD may be a displaydevice worn on the head of a user. For example, the HMD may be used torealize VR, AR or MR. Referring to FIG. 1, the first communicationdevice 910 and the second communication device 920 include processors911 and 921, memories 914 and 924, one or more Tx/Rx radio frequency(RF) modules 915 and 925, Tx processors 912 and 922, Rx processors 913and 923, and antennas 916 and 926. The Tx/Rx module is also referred toas a transceiver. Each Tx/Rx module 915 transmits a signal through eachantenna 926. The processor implements the aforementioned functions,processes and/or methods. The processor 921 may be related to the memory924 that stores program code and data. The memory may be referred to asa computer-readable medium. More specifically, the Tx processor 912implements various signal processing functions with respect to L1 (i.e.,physical layer) in DL (communication from the first communication deviceto the second communication device). The Rx processor implements varioussignal processing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the firstcommunication device) is processed in the first communication device 910in a way similar to that described in association with a receiverfunction in the second communication device 920. Each Tx/Rx module 925receives a signal through each antenna 926. Each Tx/Rx module providesRF carriers and information to the Rx processor 923. The processor 921may be related to the memory 924 that stores program code and data. Thememory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signaltransmission/reception method in a wireless communication system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, theUE performs an initial cell search operation such as synchronizationwith a BS (S201). For this operation, the UE can receive a primarysynchronization channel (P-SCH) and a secondary synchronization channel(S-SCH) from the BS to synchronize with the BS and acquire informationsuch as a cell ID. In LTE and NR systems, the P-SCH and S-SCH arerespectively called a primary synchronization signal (PSS) and asecondary synchronization signal (SSS). After initial cell search, theUE can acquire broadcast information in the cell by receiving a physicalbroadcast channel (PBCH) from the BS. Further, the UE can receive adownlink reference signal (DL RS) in the initial cell search step tocheck a downlink channel state. After initial cell search, the UE canacquire more detailed system information by receiving a physicaldownlink shared channel (PDSCH) according to a physical downlink controlchannel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radioresource for signal transmission, the UE can perform a random accessprocedure (RACH) for the BS (steps S203 to S206). To this end, the UEcan transmit a specific sequence as a preamble through a physical randomaccess channel (PRACH) (S203 and 5205) and receive a random accessresponse (RAR) message for the preamble through a PDCCH and acorresponding PDSCH (S204 and S206). In the case of a contention-basedRACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can performPDCCH/PDSCH reception (S207) and physical uplink shared channel(PUSCH)/physical uplink control channel (PUCCH) transmission (S208) asnormal uplink/downlink signal transmission processes. Particularly, theUE receives downlink control information (DCI) through the PDCCH. The UEmonitors a set of PDCCH candidates in monitoring occasions set for oneor more control element sets (CORESET) on a serving cell according tocorresponding search space configurations. A set of PDCCH candidates tobe monitored by the UE is defined in terms of search space sets, and asearch space set may be a common search space set or a UE-specificsearch space set. CORESET includes a set of (physical) resource blockshaving a duration of one to three OFDM symbols. A network can configurethe UE such that the UE has a plurality of CORESETs. The UE monitorsPDCCH candidates in one or more search space sets. Here, monitoringmeans attempting decoding of PDCCH candidate(s) in a search space. Whenthe UE has successfully decoded one of PDCCH candidates in a searchspace, the UE determines that a PDCCH has been detected from the PDCCHcandidate and performs PDSCH reception or PUSCH transmission on thebasis of DCI in the detected PDCCH. The PDCCH can be used to schedule DLtransmissions over a PDSCH and UL transmissions over a PUSCH. Here, theDCI in the PDCCH includes downlink assignment (i.e., downlink grant (DLgrant)) related to a physical downlink shared channel and including atleast a modulation and coding format and resource allocationinformation, or an uplink grant (UL grant) related to a physical uplinkshared channel and including a modulation and coding format and resourceallocation information.

An initial access (IA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2.

The UE can perform cell search, system information acquisition, beamalignment for initial access, and DL measurement on the basis of an SSB.The SSB is interchangeably used with a synchronization signal/physicalbroadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in fourconsecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH istransmitted for each OFDM symbol. Each of the PSS and the SSS includesone OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDMsymbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequencysynchronization of a cell and detects a cell identifier (ID) (e.g.,physical layer cell ID (PCI)) of the cell. The PSS is used to detect acell ID in a cell ID group and the SSS is used to detect a cell IDgroup. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group.A total of 1008 cell IDs are present. Information on a cell ID group towhich a cell ID of a cell belongs is provided/acquired through an SSS ofthe cell, and information on the cell ID among 336 cell ID groups isprovided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity.A default SSB periodicity assumed by a UE during initial cell search isdefined as 20 ms. After cell access, the SSB periodicity can be set toone of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., aBS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality ofsystem information blocks (SIBs). SI other than the MIB may be referredto as remaining minimum system information. The MIB includesinformation/parameter for monitoring a PDCCH that schedules a PDSCHcarrying SIB1 (SystemInformationBlock1) and is transmitted by a BSthrough a PBCH of an SSB. SIB1 includes information related toavailability and scheduling (e.g., transmission periodicity andSI-window size) of the remaining SIBs (hereinafter, SIBx, x is aninteger equal to or greater than 2). SiBx is included in an SI messageand transmitted over a PDSCH. Each SI message is transmitted within aperiodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will beadditionally described with reference to FIG. 2.

A random access procedure is used for various purposes. For example, therandom access procedure can be used for network initial access,handover, and UE-triggered UL data transmission. A UE can acquire ULsynchronization and UL transmission resources through the random accessprocedure. The random access procedure is classified into acontention-based random access procedure and a contention-free randomaccess procedure. A detailed procedure for the contention-based randomaccess procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of arandom access procedure in UL. Random access preamble sequences havingdifferent two lengths are supported. A long sequence length 839 isapplied to subcarrier spacings of 1.25 kHz and 5 kHz and a shortsequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz,60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BStransmits a random access response (RAR) message (Msg2) to the UE. APDCCH that schedules a PDSCH carrying a RAR is CRC masked by a randomaccess (RA) radio network temporary identifier (RNTI) (RA-RNTI) andtransmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UEcan receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH.The UE checks whether the RAR includes random access responseinformation with respect to the preamble transmitted by the UE, that is,Msg1. Presence or absence of random access information with respect toMsg1 transmitted by the UE can be determined according to presence orabsence of a random access preamble ID with respect to the preambletransmitted by the UE. If there is no response to Msg1, the UE canretransmit the RACH preamble less than a predetermined number of timeswhile performing power ramping. The UE calculates PRACH transmissionpower for preamble retransmission on the basis of most recent pathlossand a power ramping counter.

The UE can perform UL transmission through Msg3 of the random accessprocedure over a physical uplink shared channel on the basis of therandom access response information. Msg3 can include an RRC connectionrequest and a UE ID. The network can transmit Msg4 as a response toMsg3, and Msg4 can be handled as a contention resolution message on DL.The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB ora CSI-RS and (2) a UL BM procedure using a sounding reference signal(SRS). In addition, each BM procedure can include Tx beam swiping fordetermining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channelstate information (CSI)/beam is configured in RRC_CONNECTED.

A UE receives a CSI-ResourceConfig IE including CSI-S SB-ResourceSetList for SSB resources used for BM from a BS. The RRC parameter“csi-SSB-ResourceSetList” represents a list of SSB resources used forbeam management and report in one resource set. Here, an SSB resourceset can be set as {SSBx1, SSBx2, SSBx3, SSBx4, . . . }. An SSB index canbe defined in the range of 0 to 63.

The UE receives the signals on SSB resources from the BS on the basis ofthe CSI-S SB-ResourceS etList.

When CSI-RS reportConfig with respect to a report on SSBRI and referencesignal received power (RSRP) is set, the UE reports the best SSBRI andRSRP corresponding thereto to the BS. For example, when reportQuantityof the CSI-RS reportConfig IE is set to ‘ssb-Index-RSRP’, the UE reportsthe best SSBRI and RSRP corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSBand ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and theSSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’ . Here,QCL-TypeD may mean that antenna ports are quasi co-located from theviewpoint of a spatial Rx parameter. When the UE receives signals of aplurality of DL antenna ports in a QCL-TypeD relationship, the same Rxbeam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beamswiping procedure of a BS using a CSI-RS will be sequentially described.A repetition parameter is set to ‘ON’ in the Rx beam determinationprocedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of aBS.

First, the Rx beam determination procedure of a UE will be described.

The UE receives an NZP CSI-RS resource set IE including an RRC parameterwith respect to ‘repetition’ from a BS through RRC signaling. Here, theRRC parameter ‘repetition’ is set to ‘ON’.

The UE repeatedly receives signals on resources in a CSI-RS resource setin which the RRC parameter ‘repetition’ is set to ‘ON’ in different OFDMsymbols through the same Tx beam (or DL spatial domain transmissionfilters) of the BS.

The UE determines an RX beam thereof

The UE skips a CSI report. That is, the UE can skip a CSI report whenthe RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

A UE receives an NZP CSI-RS resource set IE including an RRC parameterwith respect to ‘repetition’ from the BS through RRC signaling. Here,the RRC parameter ‘repetition’ is related to the Tx beam swipingprocedure of the BS when set to ‘OFF’.

The UE receives signals on resources in a CSI-RS resource set in whichthe RRC parameter ‘repetition’ is set to ‘OFF’ in different DL spatialdomain transmission filters of the BS.

The UE selects (or determines) a best beam.

The UE reports an ID (e.g., CRI) of the selected beam and relatedquality information (e.g., RSRP) to the BS. That is, when a CSI-RS istransmitted for BM, the UE reports a CRI and RSRP with respect theretoto the BS.

Next, the UL BM procedure using an SRS will be described.

A UE receives RRC signaling (e.g., SRS-Config IE) including a (RRCparameter) purpose parameter set to ‘beam management” from a BS. TheSRS-Config IE is used to set SRS transmission. The SRS-Config IEincludes a list of SRS-Resources and a list of SRS-ResourceSets. EachSRS resource set refers to a set of SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted onthe basis of SRS-SpatialRelation Info included in the SRS-Config IE.Here, SRS-SpatialRelation Info is set for each SRS resource andindicates whether the same beamforming as that used for an SSB, a CSI-RSor an SRS will be applied for each SRS resource.

When SRS-SpatialRelationInfo is set for SRS resources, the samebeamforming as that used for the SSB, CSI-RS or SRS is applied. However,when SRS-SpatialRelationInfo is not set for SRS resources, the UEarbitrarily determines Tx beamforming and transmits an SRS through thedetermined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occurdue to rotation, movement or beamforming blockage of a UE. Accordingly,NR supports BFR in order to prevent frequent occurrence of RLF. BFR issimilar to a radio link failure recovery procedure and can be supportedwhen a UE knows new candidate beams. For beam failure detection, a BSconfigures beam failure detection reference signals for a UE, and the UEdeclares beam failure when the number of beam failure indications fromthe physical layer of the UE reaches a threshold set through RRCsignaling within a period set through RRC signaling of the BS. Afterbeam failure detection, the UE triggers beam failure recovery byinitiating a random access procedure in a PCell and performs beamfailure recovery by selecting a suitable beam. (When the BS providesdedicated random access resources for certain beams, these areprioritized by the UE). Completion of the aforementioned random accessprocedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively lowtraffic size, (2) a relatively low arrival rate, (3) extremely lowlatency requirements (e.g., 0.5 and 1 ms), (4) relatively shorttransmission duration (e.g., 2 OFDM symbols), (5) urgentservices/messages, etc. In the case of UL, transmission of traffic of aspecific type (e.g., URLLC) needs to be multiplexed with anothertransmission (e.g., eMBB) scheduled in advance in order to satisfy morestringent latency requirements. In this regard, a method of providinginformation indicating preemption of specific resources to a UEscheduled in advance and allowing a URLLC UE to use the resources for ULtransmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB andURLLC services can be scheduled on non-overlapping time/frequencyresources, and URLLC transmission can occur in resources scheduled forongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCHtransmission of the corresponding UE has been partially punctured andthe UE may not decode a PDSCH due to corrupted coded bits. In view ofthis, NR provides a preemption indication. The preemption indication mayalso be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receivesDownlinkPreemption IE through RRC signaling from a BS. When the UE isprovided with DownlinkPreemption IE, the UE is configured with INT-RNTIprovided by a parameter int-RNTI in DownlinkPreemption IE for monitoringof a PDCCH that conveys DCI format 2_1. The UE is additionallyconfigured with a corresponding set of positions for fields in DCIformat 2_1 according to a set of serving cells and positionInDCI byINT-ConfigurationPerServing Cell including a set of serving cell indexesprovided by servingCellID, configured having an information payload sizefor DCI format 2_1 according to dci-Payloadsize, and configured withindication granularity of time-frequency resources according totimeFrequencySect.

The UE receives DCI format 2_1 from the BS on the basis of theDownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configuredset of serving cells, the UE can assume that there is no transmission tothe UE in PRBs and symbols indicated by the DCI format 2_1 in a set ofPRBs and a set of symbols in a last monitoring period before amonitoring period to which the DCI format 2_1 belongs. For example, theUE assumes that a signal in a time-frequency resource indicatedaccording to preemption is not DL transmission scheduled therefor anddecodes data on the basis of signals received in the remaining resourceregion.

E. mMTC (massive MTC)

mMTC (massive Machine Type Communication) is one of 5 G scenarios forsupporting a hyper-connection service providing simultaneouscommunication with a large number of UEs. In this environment, a UEintermittently performs communication with a very low speed andmobility. Accordingly, a main goal of mMTC is operating a UE for a longtime at a low cost. With respect to mMTC, 3GPP deals with MTC and NB(NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, aPDSCH (physical downlink shared channel), a PUSCH, etc., frequencyhopping, returning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH)including specific information and a PDSCH (or a PDCCH) including aresponse to the specific information are repeatedly transmitted.Repetitive transmission is performed through frequency hopping, and forrepetitive transmission, (RF) returning from a first frequency resourceto a second frequency resource is performed in a guard period and thespecific information and the response to the specific information can betransmitted/received through a narrowband (e.g., 6 resource blocks (RBs)or 1 RB).

F. Basic Operation Between Autonomous Vehicles Using 5G Communication

FIG. 3 shows an example of basic operations of an autonomous vehicle anda 5G network in a 5G communication system.

The autonomous vehicle transmits specific information to the 5G network(S1). The specific information may include autonomous driving relatedinformation. In addition, the 5G network can determine whether toremotely control the vehicle (S2). Here, the 5G network may include aserver or a module which performs remote control related to autonomousdriving. In addition, the 5G network can transmit information (orsignal) related to remote control to the autonomous vehicle (S3).

G. Applied operations between autonomous vehicle and 5G network in 5Gcommunication system

Hereinafter, the operation of an autonomous vehicle using 5Gcommunication will be described in more detail with reference towireless communication technology (BM procedure, URLLC, mMTC, etc.)described in FIGS. 1 and 2.

First, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andeMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3, the autonomous vehicle performs aninitial access procedure and a random access procedure with the 5Gnetwork prior to step S1 of FIG. 3 in order to transmit/receive signals,information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial accessprocedure with the 5G network on the basis of an SSB in order to acquireDL synchronization and system information. A beam management (BM)procedure and a beam failure recovery procedure may be added in theinitial access procedure, and quasi-co-location (QCL) relation may beadded in a process in which the autonomous vehicle receives a signalfrom the 5G network.

In addition, the autonomous vehicle performs a random access procedurewith the 5G network for UL synchronization acquisition and/or ULtransmission. The 5G network can transmit, to the autonomous vehicle, aUL grant for scheduling transmission of specific information.Accordingly, the autonomous vehicle transmits the specific informationto the 5G network on the basis of the UL grant. In addition, the 5Gnetwork transmits, to the autonomous vehicle, a DL grant for schedulingtransmission of 5G processing results with respect to the specificinformation. Accordingly, the 5G network can transmit, to the autonomousvehicle, information (or a signal) related to remote control on thebasis of the DL grant.

Next, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andURLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemptionIE from the 5G network after the autonomous vehicle performs an initialaccess procedure and/or a random access procedure with the 5G network.Then, the autonomous vehicle receives DCI format 2_1 including apreemption indication from the 5G network on the basis ofDownlinkPreemption IE. The autonomous vehicle does not perform (orexpect or assume) reception of eMBB data in resources (PRBs and/or OFDMsymbols) indicated by the preemption indication. Thereafter, when theautonomous vehicle needs to transmit specific information, theautonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andmMTC of 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changedaccording to application of mMTC.

In step S1 of FIG. 3, the autonomous vehicle receives a UL grant fromthe 5G network in order to transmit specific information to the 5Gnetwork. Here, the UL grant may include information on the number ofrepetitions of transmission of the specific information and the specificinformation may be repeatedly transmitted on the basis of theinformation on the number of repetitions. That is, the autonomousvehicle transmits the specific information to the 5G network on thebasis of the UL grant. Repetitive transmission of the specificinformation may be performed through frequency hopping, the firsttransmission of the specific information may be performed in a firstfrequency resource, and the second transmission of the specificinformation may be performed in a second frequency resource. Thespecific information can be transmitted through a narrowband of 6resource blocks (RBs) or 1 RB.

The description of the user equipment (UE) described above can beapplied to a vehicle which will be described later with reference toFIGS. 4 to 6.

H. Vehicle, i.e., User Equipment

FIG. 4 illustrates a vehicle according to an embodiment of the presentdisclosure.

Referring to FIG. 4, a vehicle 10 according to an embodiment of thepresent disclosure is defined as means of transport traveling on roadsor railroads. The vehicle 10 means to include a car, a train and amotorcycle. The vehicle 10 may mean to include all of an internalcombustion engine vehicle having an engine as a power source, a hybridvehicle having an engine and a motor as a power source, and an electricvehicle having an electric motor as a power source, and the like. Thevehicle 10 may be a private owned vehicle. The vehicle 10 may be ashared vehicle. The vehicle 10 may be an autonomous vehicle.

The vehicle 10 according to an embodiment of the present disclosure maybe configured as a vehicle, i.e., an electric vehicle driven by anelectric motor. Thus, the vehicle 10 according to an embodiment of thepresent disclosure may be referred to as an electric vehicle 10according to an embodiment of the present disclosure and may bedesignated with the same reference numerals.

I. Block Diagram of AI Device

FIG. 5 is a block diagram of an AI device according to an embodiment ofthe present disclosure.

An AI device 20 may include an electronic device including an AI modulethat can perform AI processing, or a server including the AI module, orthe like. Further, the AI device 20 may be included as at least acomponent of the vehicle 10 illustrated in FIG. 4 to perform at least apart of the AI processing.

The AI processing may include all operations related to the control ofthe vehicle 10 illustrated in FIG. 4. For example, if the vehicle 10illustrated in FIG. 4 is an autonomous vehicle and/or an electricvehicle, the corresponding autonomous vehicle and/or electric vehiclemay perform the AI processing on sensing data or acquired data toperform a processing/determination operation and a control signalgeneration operation. Further, the corresponding autonomous vehicleand/or electric vehicle may perform the autonomous driving control byperforming AI processing on data acquired through an interaction withother electronic devices included inside the autonomous vehicle and/orthe electric vehicle or other electronic devices included outside theautonomous vehicle and/or the electric vehicle.

The AI device 20 may be a client device directly using a result of theAI processing, or a device of cloud environment providing a result ofthe AI processing for other device.

The AI apparatus 20 may include an AI processor 21, a memory 25 and/or acommunication unit 27.

The AI apparatus 20 may be a computing apparatus which may perform aneural network learning and implemented with various electronic devicessuch as a server, a desktop, a PC, a notebook PC, a tablet PC.

The AI processor 21 may perform a neural network learning using theprogram stored in the memory 25. Particularly, the AI processor 21 mayperform a neural network learning for recognizing vehicle related data.Here, the neural network for recognizing the vehicle related data may bedesigned to simulate a brain structure of a human on a computer and mayinclude a plurality of network nodes having a priority which simulatinga neuron of human neural network. A plurality of network nodes mayexchange data according to each connection relation to simulate asynaptic activity of the neuron, which the neuron exchanges a signalthrough a synapse. Here, the neural network may include a deep learningmodel which is developed from the neural network model. In the deeplearning model, a plurality of network nodes may exchange data accordingto a convolution connection relation with being located in differentlayers. An example of the neural network model may include various deeplearning techniques such as deep neural networks (DNN), convolutionaldeep neural networks (CNN), Recurrent Boltzmann Machine (RNN),Restricted Boltzmann Machine (RBM), deep belief networks (DBN), DeepQ-Network, and may be applied to a field such as computer vision, voicerecognition, natural language process and voice/signal processing.

The processer that performs the functions described above may be ageneral-purpose processor (e.g., CPU) but an AI-dedicated processor(e.g., GPU) for an artificial intelligence learning.

The memory 25 may store various types of program and data required foran operation of the AI apparatus 20. The memory 25 may be implementedwith non-volatile memory, volatile memory, flash memory, hard disk drive(HDD) or solid-state drive (SDD). The memory 25 may be accessed by theAI processor 21 and read/record/modification/deletion/update of data maybe performed by the AI processor 21. In addition, the memory 25 maystore a neurotic network model (e.g., deep learning model 26) which isgenerated through a learning algorithm for dataclassification/recognition according to an embodiment of the presentdisclosure.

The AI processor 21 may include a data learning unit 22 that learns aneurotic network for the classification/recognition. The data learningunit 22 may learn a criterion on which learning data is used todetermine the classification/recognition and how to classify andrecognize data using the learning data. The data learning unit 22 mayobtain learning data used for learning and apply the obtained learningdata to the deep learning model, and accordingly, learn the deeplearning model.

The data learning unit 22 may be manufactured in at least one hardwarechip shape and mounted on the AI apparatus 20. For example, the datalearning unit 22 may be manufactured in hardware chip shape dedicatedfor the artificial intelligence (AI) or manufactured as a part of ageneral-purpose processor (CPU) or a graphic processing processor (GPU)and mounted on the AI apparatus 20. Furthermore, the data learning unit22 may be implemented with a software module. In the case that the datalearning unit 22 is implemented with a software module (or programmodule including instruction), the software module may be stored in anon-transitory computer readable media. In this case, at least onesoftware module may be provided by an Operating System (OS) or anapplication.

The data learning unit 22 may include a learning data acquisition unit23 and a model learning unit 24.

The learning data acquisition unit 23 may acquire learning data which isrequired for the neurotic network model for classifying and recognizingdata. For example, the learning data acquisition unit 23 may obtainvehicle data and/or sample data for being inputted in the neuroticnetwork model as learning data.

The model learning unit 24 may learn to have a determination criterionhow to classify predetermined data by the neurotic network model usingthe obtained learning data. In this case, the model learning unit 24 maylearn the neurotic network model through a supervised learning that usesat least one determination criterion among learning data. Alternatively,the model learning unit 24 may learn the learning data withoutsupervising and learn the neurotic network model through an unsupervisedlearning which discovers a determination criterion. In addition, themodel learning unit 24 may learn the neurotic network model through areinforcement learning using a feedback whether a result of anassessment of situation according to learning is correct. Furthermore,the model learning unit 24 may learn the neurotic network model using alearning algorithm including an error back-propagation or a gradientdecent.

When the neurotic network model is learned, the model learning unit 24may store the learned neurotic network model in a memory. The modellearning unit 24 may store learned neurotic network model in the memoryof a server connected to the AI apparatus 20 in wired or wirelessmanner.

The data learning unit 22 may further include a learning datapre-processing unit (not shown) or a learning data selection unit (notshown) for improving an analysis result of the learning model or savinga resource or time which is required for generating a recognition model.

The learning data pre-processing unit may pre-process obtained data suchthat the obtained data is used for learning for an assessment ofsituation. For example, the learning data pre-processing unit mayprocess the obtained data in a preconfigured format such that the modellearning unit 24 uses the learning data obtained for learning an imagerecognition.

In addition, the learning data selection unit may select the datarequired for learning between the learning data obtained in the learningdata acquisition unit 23 or the learning data pre-processed in thepre-processing unit. The selected learning data may be provided to themodel learning unit 24. For example, the learning data selection unitmay detect a specific area in the image obtained through the camera andselect only the data for the object included in the specific area as thelearning data.

Furthermore, the data learning unit 22 may further include a modelevaluation unit (not shown) for improving the analysis result of thelearning model.

The model evaluation unit may input evaluation data in the neuroticnetwork model, and in the case that the analysis result fails to satisfya predetermined level, make the data learning unit 22 learn the neuroticnetwork model again. In this case, the evaluation data may be predefineddata for evaluating a recognition model. As an example, in the case thatthe number of evaluation data or the ratio in which the analysis resultis not clear exceeds a preconfigured threshold value in the analysisresult of the recognition model which is learned for the evaluationdata, the model evaluation unit may evaluate that the analysis resultfails to satisfy the predetermined level.

The communication unit 27 may the AI processing result by the AIprocessor 21 to an external electronic device.

Here, the external electronic device may be defined as an automaticdriving vehicle. In addition, the AI apparatus 20 may be defined asanother vehicle or 5G network that communicates with the automaticdriving vehicle or an automatic driving module mounted vehicle. The AIapparatus 20 may be implemented with being functionally embedded in theautomatic driving module provided in a vehicle. In addition, 5G networkmay include a server or module that performs a control in relation to anautomatic driving.

The AI apparatus 20 shown in FIG. 5 is described by functionallydividing into the AI processor 21, the memory 25 and the communicationunit 27, but the elements described above may be integrated in a moduleand called an AI module.

FIG. 6 illustrates a system, in which an autonomous vehicle isassociated with an AI device, in accordance with an embodiment of thepresent disclosure.

Referring to FIG. 6, the vehicle 10 may transmit data requiring the AIprocessing to the AI device 20 through a communication unit, and the AIdevice 20 including the deep learning model 26 may send, to the vehicle10, a result of the AI processing obtained using the deep learning model26. The AI device 20 may refer to the description with reference to FIG.5.

The vehicle 10 may include a memory 140, a processor 170 and a powersupply unit 190, and the processor 170 may include an autonomous module260 and an AI processor 261. The vehicle 10 may further include aninterface which is connected wiredly or wirelessly to at least oneelectronic device included in the vehicle 10 and can exchange datanecessary for an autonomous driving control. The at least one electronicdevice connected through the interface may include an object detector210, a communication unit 220, a driving operator 230, a main electroniccontrol unit (ECU) 240, a vehicle driver 250, a sensing unit 270, and alocation data generator 280.

The interface may be configured as at least one of a communicationmodule, a terminal, a pin, a cable, a port, a circuit, an element, or adevice.

The memory 140 is electrically connected to the processor 170. Thememory 140 can store basic data about a unit, control data for operationcontrol of a unit, and input/output data. The memory 140 can store dataprocessed in the processor 170. The memory 140 may be configuredhardware-wise as at least one of a ROM, a RAM, an EPROM, a flash drive,or a hard drive. The memory 140 can store various types of data foroverall operation of the vehicle 10, such as a program for processing orcontrol of the processor 170. The memory 140 may be integrated with theprocessor 170. According to an embodiment, the memory 140 may becategorized as a subcomponent of the processor 170.

The power supply unit 190 can provide power to the vehicle 10. The powersupply unit 190 may receive power from a power source (e.g., a battery)included in the vehicle 10 and supply power to each unit of the vehicle10. The power supply unit 190 may operate in response to a controlsignal received from the main ECU 240. The power supply unit 190 mayinclude a switched-mode power supply (SMPS).

The processor 170 may be electrically connected to the memory 140, aposition data generation unit 280 and the power supply unit 190 andexchange signals with them. The processor 170 may be implemented usingat least one of application specific integrated circuits (ASICs),digital signal processors (DSPs), digital signal processing devices(DSPDs), programmable logic devices (PLDs), field programmable gatearrays (FPGAs), processors, controllers, micro-controllers,microprocessors, or electronic units for executing other functions.

The processor 170 may be driven by power provided from the power supplyunit 190. The processor 170 may receive data, process data, generatesignals, and provide signals in a state in which power is supplied fromthe power supply unit 190.

The processor 170 may receive information from other electronic devicesof the vehicle 10 via the interface. The processor 170 may providecontrol signals to other electronic devices of the vehicle 10 via theinterface.

The vehicle 10 may include at least one printed circuit board (PCB). Thememory 140, the interface, the power supply unit 190 and the processor170 may be electrically connected to the PCB.

Other electronic devices of the vehicle 10 which are connected to theinterface, the AI processor 261, and the autonomous module 260 will bedescribed in more detail below.

The object detector 210 may generate information about objects outsidethe vehicle 10. The AI processor 261 may apply a neural network model todata acquired through the object detector 210 to generate at least oneof information on presence or absence of an object, location informationof the object, distance information of the vehicle and the object, orinformation on a relative speed between the vehicle and the object.

The object detector 210 may include at least one sensor which can detectan object outside the vehicle 10. The sensor may include at least one ofa camera, a radar, a lidar, an ultrasonic sensor, or an infrared sensor.The object detector 210 may provide data about an object generated basedon a sensing signal generated in the sensor to at least one electronicdevice included in the vehicle.

The vehicle 10 may transmit data acquired through the at least onesensor to the AI device 20 through the communication unit 220, and theAI device 20 may transmit, to the vehicle 10, AI processing datagenerated by applying the neural network model 26 to the transmitteddata. The vehicle 10 may recognize information about an object detectedbased on received AI processing data, and the autonomous module 260 mayperform an autonomous driving control operation using the recognizedinformation.

The communication unit 220 may exchange signals with devices locatedoutside the vehicle 10. The communication device 220 may exchangesignals with at least one of infrastructures (e.g., a server, abroadcasting station, etc.), other vehicles, or terminals. Thecommunication unit 220 may include at least one of a transmissionantenna, a reception antenna, a radio frequency (RF) circuit capable ofimplementing various communication protocols, or an RF element in orderto perform communication.

The AI processor 261 may apply the neural network model to data acquiredthrough the object detector 210 to generate at least one of informationon presence or absence of an object, location information of the object,distance information of the vehicle and the object, or information on arelative speed between the vehicle and the object.

The driving operator 230 is a device which receives a user input fordriving. In a manual mode, the vehicle 10 may drive based on a signalprovided by the driving operator 230. The driving operator 230 mayinclude a steering input device (e.g., a steering wheel), anacceleration input device (e.g., an accelerator pedal), and a brakeinput device (e.g., a brake pedal).

In an autonomous driving mode, the AI processor 261 may generate aninput signal of the driving operator 230 in response to a signal forcontrolling a movement of the vehicle according to a driving plangenerated through the autonomous module 260.

The vehicle 10 may transmit data necessary for control of the drivingoperator 230 to the AI device 20 through the communication unit 220, andthe AI device 20 may transmit, to the vehicle 10, AI processing datagenerated by applying the neural network model 26 to the transmitteddata. The vehicle 10 may use the input signal of the driving operator230 to control the movement of the vehicle based on the received AIprocessing data.

The main ECU 240 can control overall operation of at least oneelectronic device included in the vehicle 10.

The vehicle driver 250 is a device which electrically controls variousvehicle driving devices of the vehicle 10. The vehicle driver 250 mayinclude a power train driving control device, a chassis driving controldevice, a door/window driving control device, a safety device drivingcontrol device, a lamp driving control device, and an air-conditionerdriving control device. The power train driving control device mayinclude a power source driving control device and a transmission drivingcontrol device. The chassis driving control device may include asteering driving control device, a brake driving control device, and asuspension driving control device. The safety device driving controldevice may include a safety belt driving control device for safety beltcontrol.

The vehicle driver 250 includes at least one electronic control device(e.g., a control electronic control unit (ECU)).

The vehicle driver 250 can control a power train, a steering device, anda brake device based on signals received from the autonomous module 260.The signals received from the autonomous module 260 may be drivingcontrol signals generated by applying the neural network model tovehicle related data in the AI processor 261. The driving controlsignals may be signals received from the AI device 20 through thecommunication unit 220.

The sensing unit 270 may sense a state of the vehicle. The sensing unit270 may include at least one of an inertial measurement unit (IMU)sensor, a collision sensor, a wheel sensor, a speed sensor, aninclination sensor, a weight sensor, a heading sensor, a positionmodule, a vehicle forward/reverse sensor, a battery sensor, a fuelsensor, a tire sensor, a steering sensor, a temperature sensor, ahumidity sensor, an ultrasonic sensor, an illumination sensor, or apedal position sensor. The IMU sensor may include at least one of anacceleration sensor, a gyro sensor, or a magnetic sensor. According toembodiments of the present disclosure, sensor data may includeinformation related to a gaze direction of a driver, information relatedto a gesture that the driver lowers or raises a sun visor, and voiceinformation related to a conversation between the driver and anotherperson in the vehicle.

The AI processor 261 may apply the neural network model to sensing datagenerated in at least one sensor to generate state data of the vehicle.AI processing data generated using the neural network model may includevehicle pose data, vehicle motion data, vehicle yaw data, vehicle rolldata, vehicle pitch data, vehicle collision data, vehicle directiondata, vehicle angle data, vehicle speed data, vehicle acceleration data,vehicle inclination data, vehicle forward/reverse data, vehicle weightdata, battery data, fuel data, tire pressure data, vehicle internaltemperature data, vehicle internal humidity data, steering wheelrotation angle data, vehicle outside illumination data, pressure dataapplied to an accelerator pedal, and pressure data applied to a brakepedal, and the like. According to embodiments of the present disclosure,if a process of determining whether there is a change in the driver'sgaze is performed in the vehicle 10, the AI processing data may includeinformation related to a driver's state (e.g., a current gaze directionand a previous gaze direction of the driver, a location of the sunvisor, etc.).

The autonomous module 260 may generate a driving control signal based onAI-processed vehicle state data. According to embodiments of the presentdisclosure, if a process of determining whether there is a change in thedriver's gaze is performed by the AI processor 261, the AI drivingcontrol signal may include information determined in relation to changein the driver's gaze.

The vehicle 10 may transmit data acquired through the at least onesensor to the AI device 20 through the communication unit 220, and theAI device 20 may transmit, to the vehicle 10, AI processing datagenerated by applying the neural network model 26 to the transmitteddata.

The location data generator 280 may generate location data of thevehicle 10. The location data generator 280 can include at least one ofa global positioning system (GPS) and a differential global positioningsystem (DGPS).

The AI processor 261 can generate more accurate location data of thevehicle by applying the neural network model to location data generatedin at least one location data generating device.

According to an embodiment, the AI processor 261 may perform a deeplearning operation based on at least one of an inertial measurement unit(IMU) of the sensing unit 270 and a camera image of the object detector210 and correct location data based on the generated AI processing data.

The vehicle 10 may transmit location data acquired from the locationdata generator 280 to the AI device 20 through the communication unit220, and the AI device 20 may transmit, to the vehicle 10, AI processingdata generated by applying the neural network model 26 to the receivedlocation data.

The vehicle 10 may include an internal communication system 50. Aplurality of electronic devices included in the vehicle 10 may exchangesignals by means of the internal communication system 50. The signalsmay include data. The internal communication system 50 may use at leastone communication protocol (e.g., CAN, LIN, FlexRay, MOST, Ethernet,etc.).

The autonomous module 260 may generate a path for autonomous drivingbased on acquired data and generate a driving plan for driving along thegenerated path.

The autonomous module 260 may implement at least one advanced driverassistance system (ADAS) function. The ADAS may implement at least oneof an adaptive cruise control (ACC) system, an autonomous emergencybraking (AEB) system, a forward collision warning (FCW) system, a lanekeeping assist (LKA) system, a lane change assist (LCA) system, a targetfollowing assist (TFA) system, a blind spot detection (BSD) system, anadaptive high beam assist (HBA) system, an auto parking system (APS), aPD collision warning system, a traffic sign recognition (TSR) system, atraffic sign assist (TSA) system, a night vision (NV) system, a driverstatus monitoring (DSM) system, or a traffic jam assist (TJA) system

The AI processor 261 may send, to the autonomous module 260, a controlsignal capable of performing at least one of the aforementioned ADASfunctions by applying the neural network model to information receivedfrom at least one sensor included in the vehicle, traffic relatedinformation received from an external device, and information receivedfrom other vehicles communicating with the vehicle.

The vehicle 10 may transmit at least one data for performing the ADASfunctions to the AI device 20 through the communication unit 220, andthe AI device 20 may send, to the vehicle 10, the control signal capableof performing the ADAS functions by applying the neural network model tothe received data.

The autonomous module 260 may acquire state information of a driverand/or state information of the vehicle through the AI processor 261 andperform an operation of switching from an autonomous driving mode to amanual driving mode or an operation of switching from the manual drivingmode to the autonomous driving mode based on the acquired information.

The vehicle 10 may use AI processing data for passenger assistance fordriving control. For example, as described above, states of a driver anda passenger can be checked through at least one sensor included in thevehicle.

Further, the vehicle 10 can recognize a voice signal of a driver or apassenger through the AI processor 261, perform a voice processingoperation, and perform a voice synthesis operation.

So far, 5G communication necessary to implement the vehicle controlmethod according to an embodiment of the present disclosure, performingAI processing using the 5G communication, and transmitting/receiving aresult of AI processing have been schematically described.

With reference to FIGS. 7 to 9, a battery consumption prediction device100 of an electric vehicle according to the present disclosure isdescribed below. Before a detailed description, the vehicle 10 accordingto the present disclosure may be configured as an electric vehicle, andthe vehicle 10 and the electric vehicle may be described using the samereference numeral. In the following description, the vehicle 10 and theelectric vehicle 10 may be used interchangeably.

However, the vehicle according to the present disclosure described belowis described by way of example using an electric vehicle driven using abattery, in which power is stored, as a power source for convenience ofexplanation.

A vehicle 10 or an electric vehicle 10 according to the presentdisclosure may include an AI device 20 described above or may beconfigured as part of the AI device 20.

In this case, although not shown in FIGS. 7 to 9, a battery consumptionprediction device 100 according to the present disclosure may include anAI processor 21, a memory 25, and a communication 27 that areillustrated in FIG. 5. The battery consumption prediction device 100according to the present disclosure may include the AI device 20 that isconfigured such that the AI processor 21, the memory 25, and thecommunication 27 are integrated into one module. If the batteryconsumption prediction device 100 is configured independently of the AIdevice 20, the battery consumption prediction device 100 may beconfigured to communicate data with the AI device 20.

That is, if the battery consumption prediction device 100 according tothe present disclosure does not directly include the AI device 20, theAI device 20 may be included in an external server 300 illustrated inFIGS. 7 to 9, or may be independently configured separately from theexternal server 300.

Even if the external server 300 and the AI device 20 are configuredseparately from each other as described above, the external server 300and the AI device 20 are configured to communicate data with each other.

FIG. 7 is a block diagram illustrating configuration of a batteryconsumption prediction device of an electric vehicle according to afirst embodiment of the present disclosure. A battery consumptionprediction device 100 according to the present disclosure may include aprocessor 101 and an output unit 130, and the processor 101 may furtherinclude a collection module 110, a prediction module 120, and a learningmodule 102.

More specifically, FIG. 7 illustrates an exemplary first embodiment of abattery consumption prediction device according to the presentdisclosure, and the battery consumption prediction device according tothe exemplary first embodiment may be configured unlike configurationillustrated in FIG. 7.

That is, FIG. 7 illustrates that the battery consumption predictiondevice 100 included in the electric vehicle 10 includes one processor101, and the processor 101 includes all the collection module 110, theprediction module 120, and the learning module 102, by way of example.This is merely an exemplary configuration that is changeable.

For example, as illustrated in FIG. 8, the output unit 130 may not beincluded in the battery consumption prediction device 100 and may beimplemented as a user equipment 330. Here, the user equipment 330 mayinclude all of mobile devices capable of wireless communication, and maybe implemented as a mobile device, for example, a smart phone. In thiscase, a display 131 included in the output unit 130 may correspond to adisplay 331 included in the user equipment 330 and may equally displayinformation or data displayed on the output unit 130.

FIG. 8 is a block diagram illustrating configuration of a batteryconsumption prediction device of an electric vehicle according to asecond embodiment of the present disclosure.

As illustrated in FIG. 8, a learning module 102 may not be included in abattery consumption prediction device 100 and may be implemented to beincluded in an external server 300. The learning module 102 isimplemented as an AI device or AI module capable of machine learning,and thus may be configured as part of the battery consumption predictiondevice 100 mounted in an electric vehicle 10. However, if the learningmodule 102 is configured to provide a learning result to the batteryconsumption prediction device 100 and be able to control a predictionmodule 120 of the battery consumption prediction device 100 according tothe learning result, the learning module 102 may be implemented to beincluded in the external server 300 or implemented as a separate AIdevice or AI module independent of the external server 300.

After all, the battery consumption prediction device 100 and thelearning module 102 have to be connected to be able to communicate datawith each other. FIG. 9 is a block diagram illustrating configuration ofa battery consumption prediction device of an electric vehicle accordingto a third embodiment of the present disclosure.

With reference to FIGS. 7 to 9, the processor 101 according to anembodiment of the present disclosure predicts and calculates a batteryconsumption of the electric vehicle 10 and controls the output unit 130or the user equipment 330 so that the processor 101 outputs a result ofcalculation to the driver.

The collection module 110 included in the processor 101 collects firstinformation indicating an overall state of the electric vehicle 10 andsecond information indicating an external environment of the electricvehicle 10 and preprocesses the first information and the secondinformation to generate prediction data.

Thus, the collection module 110 is connected to be able to communicatedata with an object detector 210, a communication unit 220, a drivingoperator 230, a main ECU 240, a vehicle driver 250, a sensing unit 270,a location data generator 280, and a navigation 290 of the electricvehicle 10. The collection module 110 according to the first embodimentillustrated in FIG. 7 communicates data with the external server 300 viathe communication unit 220 and receives the second information.

Here, the first information includes a drive mode, a drive speed, thenumber of occupants, a weight of loaded load, the center of gravity, arapid acceleration history, and a rapid deceleration history of theelectric vehicle 10, and a temperature, a usage period, an output, acapacity, and a life of the battery.

Further, the second information includes a current time in a time zonein which the electric vehicle 10 is located, temperature and weatheraround the electric vehicle 10 at a current time, and a traffic state ofa route on which the electric vehicle 10 is driving.

On the other hand, as in the second embodiment illustrated in FIG. 8 andthe third embodiment illustrated in FIG. 9, the collection module 110may be connected to the external server 300 so that the collectionmodule 110 can directly communicate with the external server 300.

The collection module 110 collects raw data from the object detector210, the communication unit 220, the driving operator 230, the main ECU240, the vehicle driver 250, the sensing unit 270, the location datagenerator 280, and the navigation 290 and recognizes the raw data as thefirst information indicating an overall state of the electric vehicle10.

The first information may include various setting values of the electricvehicle 10 in addition to the raw data. A variety of sensing datameasured by the sensing unit 270 may also be collected by the collectionmodule 110 and classified as the first information.

The collection module 110 collects, from the external server 300, thesecond information that is external environment information of theelectric vehicle 10.

The second information is not information indicating the state of theelectric vehicle 10 like the first information, but is a factoraffecting the electric vehicle 10 when the electric vehicle 10 drives.Therefore, the second information is determined as a factor that mayaffect the battery consumption of the electric vehicle 10. Thedetermined factor that may affect the battery consumption of theelectric vehicle 10 is collected by the collection module 110, and isclassified as second information.

The collection module 110 is configured to collect the first informationand the second information each time a specific unit time or a specificunit distance has passed. That is, the collection module 110 collectsthe first information and the second information periodically or eachtime the electric vehicle 10 drives a predetermined distance. This is toensure the validity of data constituting the first information and thesecond information and reduce an amount of computation required for thecollection module 110 to preprocess the raw data, because a large amountof data is generated inside and outside the electric vehicle 10 everymoment.

Accordingly, the collection module 110 according to the first to thirdembodiments of the present disclosure may be configured such that a unittime is set to one time interval of 1 minute to 5 minutes and a unitdistance is set to one distance interval of 1 km to 5 km. For example,the collection module 110 according to the first embodiment of thepresent disclosure may be configured to collect the first informationand the second information at intervals of two minutes or at travelintervals of 1 km from the starting up of the electric vehicle 10.However, the setting values of the unit time and the unit distance aremerely an example and can be variously changed.

The collection module 110 classifies the collected raw data as the firstinformation and the second information, preprocesses the firstinformation and the second information to convert these information intoinput values of the learning module 102 and the prediction module 120,and generates prediction data.

The prediction module 120 according to the first to third embodiments ofthe present disclosure receives the prediction data from the collectionmodule 110, derives a predicted consumption of the battery, and obtainsa difference between the predicted consumption of the battery and anactual consumption of the battery measured in real time. Further, theprediction module 120 obtains an absolute value of the differencebetween the predicted consumption and the actual consumption in order todetermine a magnitude of the difference.

The prediction module 120 is connected to be able to communicate datawith the learning module 102 capable of performing machine learning. Thelearning module 102 may learn the first information, the secondinformation, the predicted consumption, and the actual consumption,derive associated features between them, and determine what factorgreatly affects the battery consumption. The learning module 102 givesreliability to the predicted consumption derived by the predictionmodule 120 according to the learning result.

The prediction module 120 may be configured to verify the reliabilitythat the learning module 102 gives to the predicted consumption. To thisend, the prediction module 120 determines whether the absolute value ofthe difference between the predicted consumption and the actualconsumption exceeds a predetermined magnitude. That is, the predictionmodule 120 provides a first feedback reducing the reliability of thepredicted consumption if the absolute value exceeds a first value, andprovides a second feedback increasing the reliability of the predictedconsumption if the absolute value is equal to or less than the firstvalue.

In the present disclosure, the first value may be set to one of, forexample, 5 to 10. The reliability that the learning module 102 gives tothe predicted consumption may be expressed as a natural number. Forexample, the first feedback may be configured as a feedback for adding‘−1’ to the reliability, and the second feedback may be configured as afeedback for adding ‘+1’ to the reliability.

In order to give the reliability to the predicted consumption, when thelearning module 102 learns the first information, the secondinformation, the predicted consumption, and the actual consumption, thelearning module 102 is implemented as a deep learning module that is anexample of the machine learning, and thus can extract feature valuesassociated between the first information, the second information, thepredicted consumption, and the actual consumption, give a weight valueto each feature, and check a factor that greatly affects the predictedconsumption or the actual consumption of the battery. The learningmodule 102 may additionally give a weight value to a change in thefactor and predict a battery consumption.

Accordingly, as described above, the learning module 102 itself may beimplemented as the AI device 20 in order to perform the machine learningor the deep learning, and may be included in the external server 300 orimplemented as a separate device from the external server 300. Thelearning module 102 may be implemented as the AI module and included inthe battery consumption prediction device 100 according to the presentdisclosure.

With reference to FIGS. 7 to 9, the prediction module 120 according tothe first to third embodiments of the present disclosure may beconfigured to output a consumption table 121 that uses the predictiondata as an input value and uses the predicted consumption as a resultvalue.

The consumption table 121 includes a first item unit 1201 into which thefirst information and the second information are inserted, a second itemunit 1202 that outputs a result of the first item unit 1201 and displaysa predicted consumption, and a third item unit 1203 that indicates areliability value of the predicted consumption displayed on the seconditem unit 1202.

The consumption table 121 has a function role including a predeterminedalgorithm embedded therein in order to calculate the predicted batteryconsumption, and may be created by the prediction module 120. However,the consumption table 121 may be a consumption table that is pre-storedin the prediction module 120 by a manager or a producer.

The learning module 102 constructs a data set using values input to theconsumption table 121 or prediction data to be input to the consumptiontable 121 as input values, in order to learn the first information, thesecond information, the predicted consumption, and the actualconsumption.

If the learning module 102 is implemented as a deep learning module,values input to the consumption table 121 can derive which item mostaffects the predicted consumption and the actual consumption of thebattery among items input to the first item unit 1201 of the consumptiontable 121 while passing through multiple layers including a plurality ofnodes.

The consumption table 121 illustrated in FIGS. 7 to 9 is described inmore detail. The prediction module 120 inserts prediction data, that isclassified as the first information and the second information in thecollection module 110, into each item included in the first item unit1201 of the consumption table 121.

The first item unit 1201 includes items to which information indicatingthe overall state of the electric vehicle 10 included in the firstinformation is input, and items to which information indicating anexternal environment of the electric vehicle 10 is input. Therefore,prediction data, that is classified as the first information and thesecond information in the collection module 110, is quickly and easilyinput to corresponding items or related items by the prediction module120.

In the consumption table 121 illustrated in FIGS. 7 to 9, for example,the first item unit 1201 includes both items to which the secondinformation indicating the external environment of the electric vehicle10 such as temperature and humidity is input, and items to which thefirst information indicating the overall state of the electric vehicle10 such as a drive speed is input. Since the items have been classifiedin the consumption table 121, each prediction data is immediately inputto the corresponding item.

Afterwards, the prediction module 120 processes the prediction datainput to the first item unit 1201 as a function and outputs a result ofprocessing to the second item unit 1202.

In the consumption table 121 illustrated in FIGS. 7 to 9, the seconditem unit 1202 indicates a predicted battery consumption as a result ofprocessing prediction data input per a specific unit time (e.g., atintervals of 5 minutes) in the first item unit 1201.

For example, as a result of processing prediction data input to thefirst item unit 1201 at 09:00 as a function, the second item unit 1202outputs 110 Wh/km as the predicted battery consumption. Further, as aresult of processing prediction data input to the first item unit 1201at 09:05, which is 5 minutes later, as a function, the second item unit1202 outputs 150 Wh/km as the predicted battery consumption.

The learning module 102 recognizes the predicted consumption indicatedin the second item unit 1202 and recognizes the first information andsecond information causing the output of the corresponding predictedconsumption. Afterwards, the learning module 102 searches a history ofthe consumption table 121 included in the prediction module 120 or areliability evaluation history of a specific predicted consumptionreserved by the learning module 102 itself.

If a result of evaluating before a history search result exists, thelearning module 102 may evaluate a reliability of a correspondingpredicted consumption that is recently derived based on the firstinformation and the second information included in a history, andequally give a previously evaluated reliability to the correspondingpredicted consumption.

A reliability value that the learning module 102 calculates per thepredicted consumption may be expressed as a natural number, and isindicated in the third item unit 1203 of the consumption table 121illustrated in FIGS. 7 to 9.

The prediction module 120 verifies the reliability that the learningmodule 102 gives to a specific predicted consumption. To this end, afterthe learning module 102 gives the reliability to the specific predictedconsumption, the prediction module 120 compares the specific predictedconsumption with an actual consumption, obtains a difference betweenthem, obtains an absolute value of the difference, and determines amagnitude of the difference. If the difference between the specificpredicted consumption and the actual consumption is large, theprediction module 120 generates a feedback reducing the reliability andapplies the feedback to the existing given reliability. Further, if thedifference is not large, the prediction module 120 generates a feedbackincreasing the reliability and applies the feedback to the existinggiven reliability.

A method of verifying, by the prediction module 120, the reliability isdescribed in more detail as follows. First, the collection module 110periodically measures a battery power level and collects raw data of areal-time battery power level. The prediction module 120 compares dataof an initial battery power level measured upon the vehicle starting-upwith a battery power level that is periodically measured by thecollection module 110, and derives an actual battery consumption.

Afterwards, the prediction module 120 compares a specific predictedconsumption, to which the learning module 102 gives the reliability,with the actual consumption and obtains a difference between them. Inthis instance, the prediction module 120 obtains an absolute value ofthe difference. If a magnitude of the absolute value exceeds a firstvalue, the prediction module 120 generates a first feedback reducingreliability of the specific predicted consumption that is a comparisontarget, and applies the first feedback to the reliability of thespecific predicted consumption.

On the other hand, if the magnitude of the absolute value is equal to orless than the first value, the prediction module 120 generates a secondfeedback increasing the reliability of the specific predictedconsumption that is the comparison target, and applies the secondfeedback to the reliability of the specific predicted consumption.

In this case, the first value may be set to one of, for example, 5 to10. For example, the first feedback may be set to a feedback for adding‘−1’ to the reliability value, and the second feedback may be set to afeedback for adding ‘+1’ to the reliability value. However, these valuesare merely exemplary values, and other values may be used for the firstvalue, the first feedback, and the second feedback.

A method of verifying reliability by the prediction module 120 accordingto the first to third embodiments of the present disclosure using theconsumption table 121 illustrated in FIGS. 7 to 9 as an example underthe assumption that each value has been set as described above forconvenience of explanation is described below.

In the consumption table 121 illustrated in FIGS. 7 to 9, a predictedconsumption that is output as a result of processing prediction datainput to the first item unit 1201 at 18:00 is 80 Wh/km.

The prediction module 120 can recognize that an actual consumption at18:00 is 82 Wh/km through data of a battery power level measured by thecollection module 110 at 18:00. In this case, the prediction module 120can obtain an absolute value ‘2’ of a difference between 80 Wh/km and 82Wh/km.

In this case, if a first value is 5, a difference between the predictedconsumption and the actual consumption is 2 and does not exceed thefirst value ‘5’. Based on this, the prediction module 120 can generate asecond feedback for adding ‘+1’ to a reliability value of the predictedconsumption ‘80 Wh/km’ and apply the second feedback to the reliabilityvalue of the predicted consumption ‘80 Wh/km’.

The third item unit 1203 of the consumption table 121 illustrated inFIGS. 7 to 9 can check that the reliability value of the predictedconsumption ‘80 Wh/km’ at 18:00 increased by 1.

On the contrary, the prediction module 120 can recognize that an actualconsumption at 18:00 was 92 Wh/km through data of a battery power levelmeasured by the collection module 110 at 18:00. In this case, theprediction module 120 can obtain an absolute value ‘12’ of a differencebetween 80 Wh/km and 92 Wh/km.

In this case, if the first value is 10, a difference between thepredicted consumption and the actual consumption is 12 and exceeds thefirst value ‘10’. Based on this, the prediction module 120 can generatea first feedback for adding ‘−1’ to the reliability value of thepredicted consumption ‘80 Wh/km’ and apply the first feedback to thereliability value of the predicted consumption ‘80 Wh/km’.

The first feedback and the second feedback are directly indicated in thethird item unit 1203, and thus the learning module 102 can alsorecognize the feedback applied to the reliability value.

The prediction module 120 according to the first to third embodiments ofthe present disclosure can add the feedbacks accumulated on the specificpredicted consumption through such a reliability verification functionand can determine whether the specific predicted consumption isreflected in the consumption table 121.

That is, if a predetermined number of feedbacks are applied to thereliability value through the repeated comparison between the specificpredicted consumption and the actual consumption, the prediction module120 adds the feedbacks that are accumulatively applied to thereliability of the specific predicted consumption, and calculate a sumof the feedbacks. If the feedback sum is less than a second value, theprediction module 120 deletes the specific predicted consumption fromthe consumption table 121. On the contrary, if the feedback sum isgreater than the second value, the prediction module 120 may add or fixthe specific predicted consumption to the consumption table 121.

In this case, the second value may be set to one of, for example, −5 to−10. However, this value is merely an example, and other values may beused for the second value. For example, the second value may be set to−1 to −5.

If the operation of the prediction module 120 that predicts 80 Wh/km asa predicted consumption at 18:00 and measures 92 Wh/km as an actualconsumption at 18:00 has been repeated and accumulated at least tentimes, and the second value is set to −5, a sum of feedbacks applied toa reliability value of the predicted consumption 80 Wh/km is −10 and isless than the second value.

Based on this, the prediction module 120 may determine that thereliability of the predicted consumption 80 Wh/km at 18:00 is very low,and delete the predicted consumption 80 Wh/km at 18:00 from theconsumption table 121.

On the contrary, if the operation of the prediction module 120 thatpredicts 80 Wh/km as a predicted consumption at 18:00 and measures 82Wh/km as an actual consumption at 18:00 has been repeated andaccumulated at least ten times, and the second value is set to −1, a sumof feedbacks applied to a reliability value of the predicted consumption80 Wh/km is +10 and is greater than the second value.

In this case, the prediction module 120 may determine that thereliability of the predicted consumption 80 Wh/km at 18:00 is very high,add the predicted consumption 80 Wh/km at 18:00 to the consumption table121, and utilize the first information and the second informationderiving the predicted consumption 80 Wh/km as a reference if similarprediction data occurs later.

That is, the prediction module 120 according to the present disclosureadds the first and second feedbacks accumulated on the reliability valueof the specific predicted consumption, calculates a sum of thefeedbacks, verifies the reliability value, and at the same time deletesthe specific predicted consumption from the consumption table 121 if itis determined that the reliability of the specific predicted consumptionis low. As described above, the prediction module 120 has a functioncapable of autonomously revising the consumption table 121 in additionto the reliability verification.

Further, the prediction module 120 according to the present disclosurecan calculate a battery power level based on the predicted consumptionor the actual consumption, calculate a drivable distance of the electricvehicle 10 based on the calculated battery power level, output thedrivable distance through the output unit 130 or the user equipment 330,and display the drivable distance to the driver.

That is, the output unit 130 or the user equipment 330 can displayinformation about the drivable distance and the battery power levelprovided by the prediction module 120 via a GUI including numbers,letters, symbols, and figures on the displays 131 and 331.

Accordingly, the battery consumption prediction device 100 of theelectric vehicle 10 according to the present disclosure calculates thebattery usage or the battery consumption by reflecting information aboutthe overall internal state and the external environment of the electricvehicle 10, and thus very accurately predicts the battery consumptioncompared to the related art and provides it to the driver. Hence, thedriver has greater convenience in formulating driving plans and usingthe electric vehicle.

A battery consumption prediction system 200 of an electric vehicleaccording to the present disclosure is described in detail below withreference to FIGS. 10 and 11. Structures and components of the batteryconsumption prediction system 200 according to the present disclosurethat are identical or equivalent to the battery consumption predictiondevice 100 described above are designated with the same referencenumerals, and a further description may be briefly made or may beentirely omitted.

FIG. 10 is a block diagram illustrating configuration of a batteryconsumption prediction system of an electric vehicle according to afirst embodiment of the present disclosure. FIG. 11 is a block diagramillustrating configuration of a battery consumption prediction system ofan electric vehicle according to a second embodiment of the presentdisclosure.

A battery consumption prediction system 200 of an electric vehicleaccording to the present disclosure includes a collection device 201, aprediction server 400, and a user equipment 330.

The collection device 201 collects first information indicating anoverall internal state of the electric vehicle 10 and/or secondinformation indicating an external environment of the electric vehicle10 and generates prediction data. The collection device 201 may includea processor 101 in order to collect and preprocess raw datacorresponding to the first information and/or the second information.

The processor 101 may autonomously include a collection module 110therein in order to separate the collection and the preprocessing of theraw data, but is not limited thereto. The raw data collected by thecollection module 110 may go through the preprocessing in the processor101 and may be converted into prediction data.

In the present embodiment, the processor 101 is connected to at leastone of a sensing unit, a communication unit, an object detector, adriving operator, a vehicle driver, a location data generator, anavigation, and a main ECU of the electric vehicle 10 so that theprocessor 101 can communicate data with them.

The prediction server 400 receives the prediction data from thecollection device 201 and derives a predicted battery consumption of theelectric vehicle 10. Since the prediction server 400 has functionscorresponding to the prediction module 120 of the above-describedbattery consumption prediction device 100, the prediction server 400 hasfunctions of calculating a difference between a predicted consumptionand an actual consumption of the battery and generating a feedbackchanging reliability of the predicted consumption.

However, the prediction server 400 according to the present embodimentadditionally has the function of the collection device 201 and maycollect the second information from an external server 300 andautonomously preprocess the second information to generate predictiondata.

Accordingly, the battery consumption prediction system 200 according tothe first embodiment may be configured to directly receive and processthe second information from the external server 300 instead that thecollection device 201 collects the second information from the externalserver 300. However, the prediction server 400 does not need to have thecollection function and the preprocessing function of raw data. As in abattery consumption prediction system 200 according to the secondembodiment illustrated in FIG. 11, the collection module 110 may beconfigured to be wholly responsible for the collection of raw data interms of convenience of system facilities.

A user equipment 330 is configured to be able to display the predictedconsumption, the actual consumption, a battery power level, and adrivable distance calculated by the prediction server 400.

The prediction server 400 included in the battery consumption predictionsystem 200 according to the first embodiment and the prediction server400 included in the battery consumption prediction system 200 accordingto the second embodiment each include a learning module 102 and aprediction module 120, and the learning module 102 and the predictionmodule 120 according to these embodiments correspond to the learningmodule 102 and the prediction module 120 included in the batteryconsumption prediction device 100.

Accordingly, the prediction module 120 according to the presentembodiment may output a consumption table 121 that uses prediction datagenerated by converting raw data as an input value and uses thepredicted consumption as a result value. Further, the learning module102 according to the present embodiment may learn the first informationand the second information that are factors capable of changing thepredicted consumption and the actual consumption, and may learnassociated features between the learned first information and secondinformation, the predicted consumption, and the actual consumption.

With reference to FIG. 12, the consumption table 121 is described inmore detail. FIG. 12 illustrates a consumption table according to anembodiment of the present disclosure.

The consumption table 121 includes a first item unit 1201 into which thefirst information and the second information are inserted, a second itemunit 1202 that outputs a result of the first item unit 1201 and displaysa predicted consumption, and a third item unit 1203 that indicates areliability value of the predicted consumption displayed on the seconditem unit 1202.

In the first item unit 1201, first information including data of a drivemode, a drive speed, the number of occupants, a weight of loaded load,the center of gravity, a rapid acceleration history, and a rapiddeceleration history of the electric vehicle, and a temperature, a usageperiod, an output, a capacity, and a life of the battery, and secondinformation including a current time, temperature and weather around theelectric vehicle at a current time, and a traffic state of a route, onwhich the electric vehicle is driving, are inserted in rows and columnsof a table. That is, in the first item unit 1201, items of a drive mode,a drive speed, the number of occupants, a weight of loaded load, thecenter of gravity, a rapid acceleration history, and a rapiddeceleration history of the electric vehicle, a temperature, a usageperiod, an output, a capacity, and a life of the battery, a currenttime, temperature and weather around the electric vehicle at a currenttime, a traffic state of a route, on which the electric vehicle isdriving, etc. are inserted into the rows, and data of each item isconverted into numbers and is inserted into the columns at predeterminedtime intervals.

The second item unit 1202 calculates data inserted into the first itemunit 1201 and indicates a predicted consumption of the battery. That is,the second item unit 1202 calculates numerical data, that is insertedfor each item of the first item unit 1201 in the columns at a specifictime, by a predetermined function and outputs a calculated result.

The third item unit 1203 indicates, as a numerical value, reliabilityindicating how close the predicted consumption output to the second itemunit 1202 is to the actual consumption.

Since the battery consumption prediction system 200 according to thepresent embodiment can receive prediction data from the respectiveelectric vehicles 10 at the center and provide a predicted consumptionof the battery to each electric vehicle 10, each electric vehicle 10does not need to include the learning module 102 and the predictionmodule 120. Hence, the present disclosure can reduce the individualproduction cost of the electric vehicles 10 and reduce the overallsystem equipment cost because the prediction server 400 installed at thecenter is unified and easy to manage and maintain.

A method for predicting a battery consumption of an electric vehicleaccording to the present disclosure is described in detail below withreference to FIGS. 13 to 18. In a description of a method for predictinga battery consumption of an electric vehicle according to an embodimentof the present disclosure, structures and components that are identicalor equivalent to the battery consumption prediction device 100 describedabove are designated with the same reference numerals, and a furtherdescription may be briefly made or may be entirely omitted.

FIG. 13 is a flow chart illustrating a method for predicting a batteryconsumption of an electric vehicle according to an embodiment of thepresent disclosure.

First, a driver gets on an electric vehicle 10 and starts it, and thuscan provide power to various equipments and units mounted on theelectric vehicle 10. The electric vehicle 10 is in a state capable ofstarting by starting.

Next, as illustrated in FIG. 13, if the electric vehicle 10 starts thedriving, a collection module 110 collects first information from theelectric vehicle 10 and collects second information from an externalserver 300 each time a unit time or a unit distance has passed in S110.

Next, the collection module 110 preprocesses the first information andthe second information to generate prediction data in S120. A predictionmodule 120 derives a predicted consumption of a battery of the electricvehicle 10 using the prediction data received from the collection module110 as an input value in S130.

The collection module 110 collects in real time data measuring a batterypower level of the electric vehicle 10 and transmits it to theprediction module 120. The prediction module 120 calculates an actualconsumption by subtracting a battery power level upon the measurementfrom a battery power level that has charged upon the starting, in S140.

Next, the prediction module 120 obtains a difference between the actualconsumption and the predicted consumption and an absolute value of thedifference in S150 and evaluates or verifies reliability of thepredicted consumption according to a magnitude of the absolute value inS160.

With reference to FIG. 14, the step S130 is described in more detail asfollows. FIG. 14 is a flow chart illustrating a method for deriving apredicted battery consumption according to an embodiment of the presentdisclosure.

As illustrated in FIG, 14, in the step S130, the prediction module 120creates a consumption table 121 that uses the prediction data as aninput value and uses the predicted consumption as a result value inS1310. The consumption table 121 includes a first item unit 1201 towhich the prediction data is input, a second item unit 1202 that outputsthe predicted consumption, and a third item unit 1203 that indicatesreliability of the predicted consumption.

The prediction data received from the collection module 110 is input tothe first item unit 1201 of the consumption table 121 by the predictionmodule 120 in S1311.

The predicted consumption as the result value is output to the seconditem unit 1202 by a function included in the consumption table 121 inS1312. The function processing is performed by the prediction module120.

A learning module 102 checks prediction data about the output predictedconsumption, searches a record and a history in which the learningmodule 102 evaluates reliability of the predicted consumption, or arecord and a history in which the prediction module 120 calculates thepredicted consumption so far, and searches whether data generated in thesame conditions as current prediction data exists in the history inS1313.

If there is a previous reliability evaluation result or a recordpreviously calculating a predicted consumption in S1314, a reliabilitypreviously given to the corresponding predicted consumption is given tothe corresponding predicted consumption and is indicated in the thirditem unit 1203 in S1315. If there is no previous reliability evaluationresult or no record previously calculating a predicted consumption inS1314, the prediction module 120 repeatedly calculates the predictedconsumption and waits to accumulate feedbacks of the reliability of thecorresponding predicted consumption in S1316.

With reference to FIG. 15, the step S160 is described in more detail asfollows. FIG. 15 is a flow chart illustrating a method for evaluatingreliability of a predicted consumption according to an embodiment of thepresent disclosure.

As illustrated in FIG, 15, in the step S160, the prediction module 120compares a specific predicted consumption, to which the reliability isgiven, with the actual consumption and obtains a difference between themand an absolute value of the difference. In this instance, if amagnitude of the absolute value exceeds a first value T1 in S1601, theprediction module 120 generates a first feedback reducing reliability ofthe specific predicted consumption that is a comparison target, andapplies the first feedback to the reliability of the specific predictedconsumption in S1602.

For example, the first feedback may be set to a feedback for adding ‘−1’to the reliability, and the reliability may be expressed as a naturalnumber. Further, the first value may be set to one of, for example, 5 to10.

If the magnitude of the absolute value is equal to or less than thefirst value T1 in S1601, the prediction module 120 generates a secondfeedback increasing the reliability of the specific predictedconsumption and applies the second feedback to the reliability of thespecific predicted consumption in S1603.

For example, the second feedback may be set to a feedback for adding‘+1’ to the reliability.

The following example is described with reference to the consumptiontable 121 illustrated in FIGS. 7 to 9 and FIG. 12.

In the consumption table 121 illustrated in FIGS. 7 to 9 and FIG. 12, apredicted consumption that is output as a result of processingprediction data input to the first item unit 1201 at 18:00 is 80 Wh/km.

The prediction module 120 can recognize that an actual consumption at18:00 is 82 Wh/km through data of a battery power level measured by thecollection module 110 at 18:00. In this case, the prediction module 120can obtain an absolute value ‘2’ of a difference between 80 Wh/km and 82Wh/km.

In this case, if the first value is 5, a difference between thepredicted consumption and the actual consumption is 2 and does notexceed the first value ‘5’. Based on this, the prediction module 120 cangenerate a second feedback for adding ‘+1’ to a reliability value of thepredicted consumption ‘80 Wh/km’ and apply the second feedback to thereliability value of the predicted consumption ‘80 Wh/km’.

Afterwards, the prediction module 120 may repeat the step S160 includingthe steps S1601, S1602 and S1603. If the step S160 is repeatedlyperformed predetermined times and feedbacks of the reliability of thespecific predicted consumption are accumulated in S161, the predictionmodule 120 adds the first and second feedbacks accumulated on thecorresponding reliability and calculates a sum of the feedbacks in S170.

If the feedback sum is less than a second value T2 in S180, theprediction module 120 deletes a predicted consumption corresponding tothe feedback sum from the consumption table 121 in S181. In this case,the second value T2 may be set to one of, for example, −5 to −10.

If the feedback sum is greater than the second value T2 in S180, theprediction module 120 adds a predicted consumption corresponding to thefeedback sum to the consumption table 121 in S182.

For example, if the operation of the prediction module 120 that predicts80 Wh/km as a predicted consumption at 18:00 and measures 82 Wh/km as anactual consumption at 18:00 has been repeated and accumulated at leastten times, and the second value is set to −1, a sum of feedbacks appliedto a reliability value of the predicted consumption 80 Wh/km is +10 andis greater than the second value.

In this instance, the prediction module 120 may determine that thereliability of the predicted consumption 80 Wh/km at 18:00 is very high,add the predicted consumption 80 Wh/km at 18:00 to the consumption table121, and utilize the first information and the second informationderiving the predicted consumption 80 Wh/km as a reference if similarprediction data occurs later.

With reference to FIG. 17, a method for indicating, by a batteryconsumption prediction device 100 or a battery consumption predictionsystem 200 according to the present disclosure, a drivable distancebased on a battery power level is described. FIG. 17 is a flow chartillustrating a method for indicating a drivable distance according to anembodiment of the present disclosure.

After the step S160, the prediction module 120 calculates a currentbattery power level of the electric vehicle 10 based on the predictedconsumption or the actual consumption in S190 and calculates a drivabledistance of the electric vehicle 10 based on the current battery powerlevel in S191. Next, the drivable distance is displayed to a driverthrough an output unit 130 or a user equipment 330 in S192.

With reference to FIG. 18, a method for predicting a battery consumptionof an electric vehicle according to another embodiment of the presentdisclosure is described. FIG. 18 is a flow chart illustrating a methodfor predicting a battery consumption of an electric vehicle according toanother embodiment of the present disclosure.

First, a driver gets on an electric vehicle 10 and starts it, and thuscan provide power to various equipments and units mounted on theelectric vehicle 10. The electric vehicle 10 is in a state capable ofstarting by starting.

Next, the driver may input a destination to a navigation 290 installedin the electric vehicle 10 in S200. If the driver does not input thedestination to the navigation 290 in S200, a prediction module 120calculates a predicted consumption of a battery using first informationand second information as in the flow chart illustrated in FIG. 13.Since this was described above, a duplicate description is omitted.

On the other hand, if the driver inputs the destination to thenavigation 290 in 5200, the process is performed as illustrated in FIG.17.

The navigation 290 may output one or more routes for reaching thedestination in S201 and recommend the routes to the driver. If thedriver selects one route from among the recommended routes, a collectionmodule 110 collects third information about the route selected by thedriver. The third information includes a total length of the routeselected by the driver, a type of road installed in the correspondingroute, and a slope, an altitude above sea level, an altitude deviationand a terrain for each section included in the corresponding route.

The collection module 110 collects and preprocesses raw data included inthe third information from the navigation 290 and/or an external server300, and then generates prediction data of the third information in5220.

At the same time, the collection module 110 collects first informationfrom the components included in the electric vehicle 10, for example, anobject detector 210, a communication unit 220, a driving operator 230, amain ECU 240, a vehicle driver 250, a sensing unit 270, a location datagenerator 280, and the navigation 290 and collects second informationindicating an external environment of the electric vehicle 10 from theexternal server 300 in S210.

Next, the collection module 110 preprocesses the collected firstinformation and second information and generates prediction data of thefirst information and the second information in S220.

The prediction module 120 receives, from the collection module 110, boththe prediction data of the first information and the second informationand the prediction data of the third information to output a predictedconsumption, to which all the first to third information is reflected,as a result value using these prediction data as input values of aconsumption table 121, in S230. In this instance, the step S230 mayinclude the steps S1310, S1311, S1312, S1313, S1314, S1315 and S1316included in the step S130.

That is, in the present embodiment, the step S230 of deriving thepredicted consumption of the battery may performed including the stepsS1310, S1311, S1312, S1313, S1314, S1315 and S1316 described in theprevious embodiment.

The collection module 110 measures a battery power level in real timeusing at least one of the main ECU 240, the vehicle driver 250, and thesensing unit 270 and transmits measured data to the prediction module120.

The prediction module 120 compares the measured data of the batterypower level and a battery power level measured upon the starting of theelectric vehicle 10 to calculate an actual consumption in S240.

The prediction module 120 obtains a difference between the actualconsumption and the predicted consumption and an absolute value of thedifference in S250.

Next, the prediction module 120 evaluates reliability of the predictedconsumption according to a magnitude of the absolute value in S260. Inthis instance, the step S260 may include the steps S1601, S1602 andS1603 included in the step S160, and the steps S161, S170, S180, S181and S182 may be performed after the step S260.

That is, the step S160 includes the step S1601 that the predictionmodule 120 determines whether the absolute value of the differencebetween the actual consumption and the predicted consumption exceeds afirst value T1.

For example, if the prediction module 120 has calculated a differencebetween a predicted consumption and an actual consumption at a firsttime and has obtained an absolute value of the difference, theprediction module 120 determines whether the absolute value exceeds thefirst value T1 in the step S1601. A range of the first value T1 refersto a description of a battery consumption prediction device 100according to an embodiment of the present disclosure.

If the absolute value exceeds the first value T1, the prediction module120 applies a first feedback to a reliability value of the predictedconsumption at the first time and reduces the reliability value of thepredicted consumption at the first time in S1602.

On the other hand, if the absolute value is equal to or less than thefirst value T1, the prediction module 120 applies a second feedback to areliability value of the predicted consumption at the first time andincreases the reliability value of the predicted consumption at thefirst time in S1603.

In the reliability value, if a predicted consumption is derived in asecond item unit 1201 of the consumption table 121 included in theprediction module 120, the learning module 102 may search a previoushistory, search whether the same predicted consumption has been outputunder conditions where the same prediction data is input, and give apredetermined reliability to the predicted consumption if the predictedconsumption is the same as a previously output predicted consumptionaccording to a search result.

The prediction module 120 can verify the reliability that the learningmodule 102 gives to the predicted consumption. To this end, theprediction module 120 utilizes feedback values accumulated on thepredicted consumption.

That is, the prediction module 120 repeatedly performs the step S160 toaccumulate feedbacks on the predicted consumption in S161. If the numberof accumulated feedbacks exceeds a predetermined number that ispreviously determined by a manager, the prediction module 120 adds allthe accumulated feedbacks in S170.

Next, the prediction module 120 determines whether a sum of thefeedbacks is less than a second value T2, in S180. A range of the secondvalue refers to the description of the battery consumption predictiondevice 100 according to an embodiment of the present disclosure.

If the feedback sum is less than the second value T2, the predictionmodule 120 deletes a predicted consumption corresponding to the feedbacksum from the consumption table 121 in S181. On the other hand, if thefeedback sum is greater than the second value T2, the prediction module120 adds a predicted consumption corresponding to the feedback sum tothe consumption table 121 in S182.

As described above, the battery consumption prediction device, thebattery consumption prediction system, and the battery consumptionprediction method according to the present disclosure calculate abattery consumption consumed to drive the route considering an externalenvironment of the electric vehicle. Since the battery consumptionprediction device, system, and method according to the presentdisclosure output a drivable distance according to the batteryconsumption and display the drivable distance to the driver, the drivercan check the predicted consumption of the battery displayed on theoutput unit 130 and previously know a battery amount consumed tocomplete from a departure to the destination

Further, the battery consumption prediction system 200 according to thepresent disclosure can calculate a route with a minimum batteryconsumption and recommend the route to the driver when calculating aroute recommendable to the driver if the driver inputs a destination tothe navigation 290.

In addition, there may occur a case in which as the traffic conditionsor the weather such as snow or rain change while the driver inputs adestination to the navigation 290 and drives along the route, morebattery consumption may be required than the total battery consumptionthat is initially predicted to complete the route.

In this instance, the battery consumption prediction system 200according to the present disclosure requests the navigation 290 tocreate a new route and allows the navigation 290 to create a route froma place where the electric vehicle 10 is currently located to thedestination, which can minimize an additional battery consumption, andto display the route to the driver.

As described above, the battery consumption prediction device, system,and method of the electric vehicle according to the present disclosureaccurately predict how much battery the electric vehicle can use whiledriving and provide it to the driver, and thus can make the use ofelectric vehicle more convenient.

The present disclosure may be implemented using a computer-readablemedium with programs recorded thereon for execution by a processor toperform various methods presented herein. The computer-readable mediumincludes all kinds of recording devices capable of storing data that isreadable by a computer system. Examples of the computer-readable mediumsinclude hard disk drive (HDD), solid state disk (SSD), silicon diskdrive (SDD), ROM, RAM, CD-ROM, a magnetic tape, a floppy disk, anoptical data storage device, the other types of storage mediumspresented herein, and combinations thereof. If desired, thecomputer-readable medium may be realized in the form of a carrier wave(e.g., transmission over Internet). Thus, the foregoing description ismerely an example and is not to be considered as limiting the presentdisclosure. The scope of the present disclosure should be determined byrational interpretation of the appended claims, and all changes withinthe equivalent range of the present disclosure are included in the scopeof the present disclosure.

What is claimed is:
 1. A battery consumption prediction device of anelectric vehicle, the battery consumption prediction device comprising:at least one processor configured to calculate a battery consumption ofthe electric vehicle, wherein the at least one processor includes: acollection module configured to collect first information indicating anoverall state of the electric vehicle and second information indicatingan external environment of the electric vehicle, and generate predictiondata based on the first information and the second information; and aprediction module configured to receive the prediction data from thecollection module and derive a predicted consumption of a battery of theelectric vehicle.
 2. The battery consumption prediction device of claim1, wherein the prediction module is further configured to: obtain adifference between the predicted consumption and an actual consumptionof the battery that is calculated by measuring the battery in real-time,and an absolute value of the difference; provide a first feedback thatreduces a reliability value of the predicted consumption if the absolutevalue of the difference exceeds a first value; and provide a secondfeedback that increases the reliability value of the predictedconsumption if the absolute value is equal to or less than the firstvalue.
 3. The battery consumption prediction device of claim 2, whereinthe at least one processor further includes a learning module that isconnected to be able to communicate with the collection module and theprediction module, wherein the learning module is configured tomachine-learn the first information, the second information, thepredicted consumption, and the actual consumption, calculate thedifference between the predicted consumption and the actual consumptionand the absolute value of the difference, and output the reliabilityvalue of the predicted consumption according to a magnitude of theabsolute value.
 4. The battery consumption prediction device of claim 2,wherein the prediction module is further configured to output aconsumption table that uses the prediction data as an input value anduses the predicted consumption as a result value.
 5. The batteryconsumption prediction device of claim 4, wherein the consumption tableincludes: a first item unit into which the first information and thesecond information are inserted; a second item unit indicating thepredicted consumption as a result of the first item unit; and a thirditem unit indicating the reliability value of the predicted consumptionindicated by the second item unit.
 6. The battery consumption predictiondevice of claim 2, wherein the reliability value is expressed as anatural number, wherein the first feedback is a feedback for adding ‘−1’to the reliability value, and wherein the second feedback is a feedbackfor adding ‘+1’ to the reliability value.
 7. The battery consumptionprediction device of claim 6, wherein the prediction module is furtherconfigured to: output a consumption table that uses the prediction dataas an input value and uses the predicted consumption as a result value;and add the first feedback and the second feedback accumulated on thereliability value to calculate a feedback sum.
 8. The batteryconsumption prediction device of claim 7, wherein the prediction moduleis further configured to delete the predicted consumption correspondingto the feedback sum from the consumption table if the feedback sum isless than a second value that is set to a value in a range of −5 to −10.9. The battery consumption prediction device of claim 7, wherein theprediction module is further configured to add the predicted consumptioncorresponding to the feedback sum to the consumption table if thefeedback sum is greater than a second value that is set to a value in arange of −5 to −1.
 10. The battery consumption prediction device ofclaim 2, wherein the first value is set to a value in a range of 5 to10.
 11. The battery consumption prediction device of claim 1, whereinthe collection module is connected to be able to communicate with atleast one of a sensing unit, a communication unit, an object detector, adriving operator, a vehicle driver, a location data generator, anavigation, or a main electronic control unit (ECU) of the electricvehicle, and wherein if a time interval or a distance interval haspassed, the collection module is further configured to collect the firstinformation and the second information from at least one of the sensingunit, the communication unit, the object detector, the driving operator,the vehicle driver, the location data generator, the navigation, or themain ECU of the electric vehicle.
 12. The battery consumption predictiondevice of claim 11, wherein the time interval is set to an interval in arange of 1 minute to 5 minutes, and wherein the distance interval is setto an interval in a range of 1 km to 5 km.
 13. The battery consumptionprediction device of claim 1, wherein the first information includes adrive mode, a drive speed, a number of occupants, a weight of loadedload, a center of gravity, a rapid acceleration history and a rapiddeceleration history of the electric vehicle, and a temperature, a usageperiod, an output, a capacity and a life of the battery.
 14. The batteryconsumption prediction device of claim 1, wherein the second informationincludes a current time, a temperature and a weather condition aroundthe electric vehicle at the current time, and a traffic state of a routeon which the electric vehicle is driving.
 15. The battery consumptionprediction device of claim 1, further comprising an output displayconfigured to display a battery power level calculated based on thepredicted consumption or the actual consumption, and display a drivabledistance of the electric vehicle based on the battery power level.
 16. Abattery consumption prediction system of an electric vehicle, thebattery consumption prediction system comprising: a collection deviceconfigured to collect first information indicating an overall state ofthe electric vehicle and second information indicating an externalenvironment of the electric vehicle, and generate prediction data; aprediction server configured to derive a predicted consumption of abattery of the electric vehicle based on the prediction data generatedby the collection device; and a user equipment configured to display aresult calculated by the prediction server, wherein the predictionserver is further configured to calculate a difference between thepredicted consumption and an actual consumption of the battery, andgenerate a feedback changing a reliability value of the predictedconsumption.
 17. The battery consumption prediction system of claim 16,wherein the collection device includes at least one processor configuredto collect raw data of the electric vehicle as the first information,preprocess the first information, and generate the prediction data,wherein the at least one processor is connected to be able tocommunicate with at least one of a sensing unit, a communication unit,an object detector, a driving operator, a vehicle driver, a locationdata generator, a navigation, or a main electronic control unit (ECU) ofthe electric vehicle.
 18. The battery consumption prediction system ofclaim 17, wherein the at least one processor is further configured to,periodically or each time the electric vehicle drives a predetermineddistance: collect the raw data from at least one of the sensing unit,the communication unit, the object detector, the driving operator, thevehicle driver, the location data generator, the navigation, or the mainECU; and collect the second information from an external server.
 19. Thebattery consumption prediction system of claim 18, wherein the firstinformation includes a drive mode, a drive speed, a number of occupants,a weight of loaded load, a center of gravity, a rapid accelerationhistory and a rapid deceleration history of the electric vehicle, and atemperature, a usage period, an output, a capacity and a life of thebattery, wherein the second information includes a current time, atemperature and a weather condition around the electric vehicle at thecurrent time, and a traffic state of a route on which the electricvehicle is driving.
 20. The battery consumption prediction system ofclaim 16, wherein the prediction server includes: a learning moduleconfigured to machine-learn the first information and the secondinformation, that are factors capable of changing the predictedconsumption and the actual consumption, in association with thepredicted consumption and the actual consumption; and a predictionmodule configured to output a consumption table that uses the predictiondata as an input value and uses the predicted consumption as a resultvalue.
 21. The battery consumption prediction system of claim 20,wherein the prediction module is further configured to: obtain adifference between the predicted consumption and the actual consumptionthat is calculated by measuring the battery in real-time, and anabsolute value of the difference; provide a first feedback that reducesthe reliability value of the predicted consumption if the absolute valueof the difference exceeds a first value; and provide a second feedbackthat increases the reliability value of the predicted consumption if theabsolute value is equal to or less than the first value.
 22. The batteryconsumption prediction system of claim 16, further comprising anexternal server configured to transmit the second information to thecollection device.
 23. A method for predicting a battery consumption ofan electric vehicle, the method comprising: collecting first informationand second information; preprocessing the first information and thesecond information to generate prediction data; deriving a predictedconsumption of a battery of the electric vehicle using the predictiondata as an input value; measuring in real-time a battery power level ofthe electric vehicle and subtracting the measured battery power levelfrom an initial battery power level to calculate an actual consumption;obtaining a difference between the predicted consumption and the actualconsumption and an absolute value of the difference; and evaluating areliability of the predicted consumption according to a magnitude of theabsolute value of the difference.
 24. The method of claim 23, whereinevaluating the reliability comprises: applying a first feedback thatreduces a reliability value of the predicted consumption if the absolutevalue of the difference exceeds a first value; and applying a secondfeedback that increases the reliability value of the predictedconsumption if the absolute value is equal to or less than the firstvalue.
 25. The method of claim 24, wherein the first value is set to avalue in a range of 5 to
 10. 26. The method of claim 24, wherein thefirst feedback is a feedback for adding ‘−1’ to the reliability value,wherein the second feedback is a feedback for adding ‘+1’ to thereliability value.
 27. The method of claim 23, wherein deriving thepredicted consumption comprises: creating a consumption table that usesthe prediction data as an input value and uses the predicted consumptionas a result value; inputting the prediction data to the consumptiontable; outputting the predicted consumption as a result value; searchinga reliability evaluation history and checking whether there is aprevious reliability evaluation result corresponding to the predictedconsumption output as the result value; and if the previous reliabilityevaluation result exists in the reliability evaluation history,providing and displaying a reliability value included in the previousreliability evaluation result.
 28. The method of claim 23, furthercomprising, after evaluating the reliability: adding a first feedbackand a second feedback accumulated on a reliability value of thepredicted consumption to calculate a feedback sum; and deleting thepredicted consumption corresponding to the feedback sum from aconsumption table if the feedback sum is less than a first value. 29.The method of claim 28, wherein the first value is set to a value in arange of −5 to −10.
 30. The method of claim 28, further comprising,after calculating the feedback sum, adding the predicted consumptioncorresponding to the feedback sum to the consumption table if thefeedback sum is greater than the first value.
 31. The method of claim23, further comprising, after evaluating the reliability: calculating acurrent battery power level of the electric vehicle based on thepredicted consumption or the actual consumption; calculating a drivabledistance of the electric vehicle based on the current battery powerlevel; and displaying the drivable distance to a driver.
 32. The methodof claim 23, further comprising, before collecting the first informationand the second information: inputting a destination to a navigation ofthe electric vehicle; outputting at least a first route for reaching thedestination; and collecting third information about the first route. 33.The method of claim 32, wherein deriving the predicted consumptioncomprises: calculating a predicted battery consumption with respect tothe first route based on the third information; and displaying, to thedriver, a total battery consumption consumed to complete the firstroute, wherein the third information includes a total length of thefirst route, a type of road installed in the first route, a slope, analtitude above sea level, an altitude deviation and a terrain for eachsection included in the first route.